A biological sensor pathogen high-sensitivity detection system and a detection method thereof

Abstract

The present application relates to the technical field of biosensor micro-detection, in particular to a biosensor pathogen high-sensitivity detection system and a detection method thereof, comprising a micro-detection and pretreatment module, a high-sensitivity biosensor core module, a micro-fluidic reaction and signal amplification module, a low-noise signal acquisition and conversion module, and a data analysis and micro-interpretation module. The present application realizes efficient synchronous pretreatment of complex matrix micro-samples through multi-channel parallel sampling, multi-layer gradient filter membrane fractionation, magnetic bead targeted enrichment, and standardized washing and elution, effectively removes impurities and interference, and obtains high-purity and high-concentration test bacteria solution, realizes multiple cascade amplification of target recognition signal, significantly improves the sensitivity of pathogen detection, and can effectively capture the weak signal of extremely low copy number pathogen.

Description

Technical Field

[0001] This invention relates to the field of biosensor micro-detection technology, specifically to a biosensor-based high-sensitivity pathogen detection system and its detection method. Background Technology

[0002] Pathogens refer to a collective term for various pathogenic microorganisms that can cause diseases in humans, animals, or plants, mainly including pathogenic bacteria, harmful fungi, and infectious viruses. These microorganisms are tiny in size and widely distributed in the environment, spreading through various routes such as water, food, respiratory tract, and contact media. Once they invade the body of humans, animals, or plants, they disrupt normal physiological metabolism and immune balance, easily inducing various infectious diseases and seriously threatening public health, food safety, and the health of living organisms. Therefore, rapid, trace, and highly sensitive detection of pathogens in various complex matrix samples has become an essential key technology in many fields such as water quality monitoring, food safety screening, clinical body fluid pathogen diagnosis, and public health prevention. Current technologies, when detecting trace samples in complex matrices containing pathogens, generally suffer from poor pretreatment effects and severe interference from matrix impurities. Furthermore, the target recognition response signals for pathogens are relatively weak, and conventional detection methods have limited signal amplification capabilities, resulting in low overall detection sensitivity and making it difficult to achieve accurate identification and quantitative detection of pathogens with extremely low copy numbers.

[0003] Based on this, the present invention provides a highly sensitive biosensor-based pathogen detection system and method to solve the aforementioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a highly sensitive biosensor-based bacterial detection system and method. This invention achieves efficient and simultaneous pretreatment of trace samples in complex matrices through multi-channel parallel sampling, multi-layer gradient filtration for graded impurity removal, and magnetic bead-targeted enrichment followed by standardized washing and elution. This effectively removes interference from impurities and obtains high-purity, high-concentration bacterial solutions, providing a high-quality sample basis for subsequent detection. Furthermore, by precisely controlling fluid reaction conditions through microchannels, and combining a DNA nanoscaffold-loaded nanozyme catalytic cascade system and a negative signal conversion mechanism, multiple cascade amplification of target recognition signals is achieved, significantly improving the sensitivity of bacterial detection and effectively capturing weak signals from extremely low copy number pathogens.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a highly sensitive biosensor-based pathogen detection system, comprising a trace detection and preprocessing module, a highly sensitive biosensor core module, a microfluidic reaction and signal amplification module, a low-noise signal acquisition and conversion module, and a data analysis and trace interpretation module, wherein: The trace detection and preprocessing module is used to carry a multi-layer gradient microporous filtration and magnetic bead targeted enrichment linkage structure and integrate a multi-channel parallel sampling pipeline to perform stratified impurity removal, targeted enrichment of pathogens, and simultaneous preprocessing of multiple samples in complex matrix trace samples. The highly sensitive biosensing core module is used to convert the weak biological response signal generated by the specific capture of pathogens into a detectable physical signal based on specific bio-recognition elements and sensing transducers. The microfluidic reaction and signal amplification module is used to regulate the reaction fluid through a microchannel network and to construct a catalytic cascade reaction system by loading nanozymes onto DNA nanoscaffolds. Through the negative signal conversion mechanism of the enzyme catalytic products synergistically decomposing the chromogenic substrate, it performs multiple cascade amplification of the target recognition signal in a small volume. The low-noise signal acquisition and conversion module is used to perform high-gain, low-noise conditioning, filtering, and analog-to-digital conversion on the weak analog signal output by the sensor, and to extract the effective detection signal. The data analysis and trace quantity interpretation module performs zero-point drift correction and nonlinear fitting calculation on the detection signal based on the built-in analysis algorithm, determines the concentration threshold for extremely low copy number pathogens, and outputs the quantitative detection results of pathogens.

[0006] The trace detection and preprocessing module includes a multi-layer gradient microporous filtration unit, a magnetic bead targeted enrichment linkage unit, and a multi-channel parallel sampling unit, wherein: The multi-layer gradient microporous filtration unit is used to perform physical hierarchical filtration of complex matrix samples through multi-layer filter membranes with decreasing pore size. The magnetic bead targeted enrichment linkage unit is used to efficiently capture, separate, and enrich target pathogens under magnetic field control by utilizing magnetic beads with surface modifications that specifically recognize molecules. The multi-channel parallel sampling unit is used to integrate multiple independent sampling pipelines to simultaneously absorb and preprocess multiple samples.

[0007] The multilayer gradient microporous filtration unit comprises at least a large-pore pre-filtration layer, a medium-pore interception layer, and a small-pore fine filtration layer arranged sequentially along the sample flow direction; wherein the pore size of the large-pore pre-filtration layer is 5-50 μm, the pore size of the medium-pore interception layer is 0.1-5 μm, and the pore size of the small-pore fine filtration layer is 0.01-0.1 μm.

[0008] The highly sensitive biosensor core module includes a specific biometric identification unit and a sensing transducer unit, wherein: The specific biometric unit is used to immobilize recognition elements for antibodies, aptamers, and receptors, specifically capturing target pathogens and generating biological response signals. The sensing transducer unit is used to convert the weak biological signals generated by pathogen capture into measurable physical signals.

[0009] The microfluidic reaction and signal amplification module includes a microchannel fluid control unit, a DNA nanoscaffold loading unit, a catalytic cascade reaction unit, and a negative signal conversion unit, wherein: The microchannel fluid control unit is used to precisely control the flow path, mixing, and residence time of the reaction fluid by designing a micron-level channel network. The DNA nanoscaffold loading unit is used to load nanozymes in an orderly manner using DNA self-assembled nanostructures as carriers to achieve localized high-concentration catalytic sites. The catalytic cascade reaction unit is used to construct a multi-step enzyme catalytic reaction chain, so that the product of the previous stage becomes the substrate of the next stage for signal amplification step by step. The negative signal conversion unit is used to establish a negative correlation between target concentration and detection signal intensity by synergistically decomposing the chromogenic substrate through enzyme catalysis products.

[0010] The DNA nanoscaffold loading unit uses DNA self-assembled nanostructures as carriers to orderly load nanozymes for localized high-concentration catalytic sites. The specific operation is as follows: A1: Tetrahedral DNA nanoscaffolds are formed by self-assembly of single-stranded DNA through base complementary pairing, and the surface of the tetrahedral DNA nanoscaffolds is modified with amino active sites. A2: The nanozyme is dispersed in a buffer solution and then anchored to the active site of a tetrahedral DNA nanoscaffold through amino-carboxyl covalent bonding. A3: By adjusting the reaction temperature to 37℃ and the reaction time to 2-3h, nanozymes are loaded onto DNA nanoscaffolds in a high-density and orderly manner, forming local high-concentration catalytic sites.

[0011] The catalytic cascade reaction unit constructs a multi-step enzyme catalytic reaction chain, so that the product of the previous stage becomes the substrate of the next stage for stepwise signal amplification. The specific operation is as follows: B1: Nanozymes loaded on DNA nanoscaffolds act as first-stage catalysts, catalyzing the oxidation of glucose to hydrogen peroxide and lowering the pH of the reaction system. B2: The generated hydrogen peroxide acts as an oxidant in the second-order reaction under acidic conditions, co-catalyzing the decomposition of the chromogenic substrate with characteristic absorption peaks. Nanosheets, transforming them into colorless ; B3: As the concentration of the target pathogen increases, the chromogenic substrate... The nanosheets are decomposed in large quantities, resulting in a significant decrease in the absorbance or electrical signal intensity of the detection system, leading to negative signal conversion and stepwise amplification of substrate consumption.

[0012] The low-noise signal acquisition and conversion module includes a high-gain low-noise conditioning unit, a filtering unit, an analog-to-digital conversion unit, and an effective signal extraction unit, wherein: The high-gain, low-noise conditioning unit is used to pre-amplify and suppress common-mode noise in the weak analog signal output by the sensor, thereby improving the signal-to-noise ratio. The filtering unit is used to filter out high-frequency noise and interference frequency components in the signal through a low-pass or band-pass filter. The analog-to-digital conversion unit is used to convert the conditioned and filtered continuous analog signal into a discrete digital signal. The effective signal extraction unit is used to identify and extract characteristic signal components related to pathogen concentration from the digital signal.

[0013] The data analysis and trace interpretation module includes a zero-point drift correction unit, a nonlinear fitting calculation unit, an extremely low copy threshold determination unit, and a quantitative result output unit, wherein: The zero-point drift correction unit eliminates signal zero-point drift caused by the environment based on real-time baseline signal monitoring and algorithm compensation. The nonlinear fitting operation unit is used to perform nonlinear regression fitting on the signal-concentration relationship using a standard curve. The extremely low copy threshold determination unit is used to classify and determine whether a weak signal is higher than the background noise based on a preset signal interval and detection limit. The quantitative result output unit is used to convert the judgment result into a pathogen concentration value and output it through a display or data interface.

[0014] This invention also proposes a highly sensitive biosensor method for detecting pathogens, comprising the following steps: S1: Gradient gradation filtration, impurity removal and targeted enrichment of target pathogens with magnetic beads are performed on micro samples of complex multi-channel matrix to obtain high-purity samples of pathogens to be tested. S2: The target pathogen is captured by a specific recognition element and a biological response signal is generated. The weak biological response signal is then converted into a detectable physical signal by a sensor transducer. S3: Based on the precise control of fluid reaction conditions by microfluidic channels, a multi-level catalytic cascade system is constructed by loading nanozymes onto DNA nanoscaffolds, and the negative correlation between target concentration and detection signal is amplified step by step by means of the consumption of chromogenic substrate; S4: Perform high-gain low-noise conditioning, filtering and noise reduction, and analog-to-digital conversion on the weak analog signal output by the sensor, remove interference, and extract effective feature signals related to the concentration of pathogens. S5: Perform zero-point drift compensation and nonlinear fitting calculation on the acquired signal, determine the threshold of extremely low copy number pathogens according to the preset detection limit confidence interval, and output accurate quantitative detection results of pathogens.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves efficient and simultaneous pretreatment of trace samples in complex matrices through multi-channel parallel sampling, multi-layer gradient filtration membrane grading for impurity removal, and magnetic bead targeted enrichment followed by standardized washing and elution. This effectively removes interference from impurities and obtains high-purity, high-concentration bacterial solutions for testing, providing a high-quality sample foundation for subsequent detection. Furthermore, by precisely controlling fluid reaction conditions through microchannels, and combining a DNA nanoscaffold-loaded nanozyme catalytic cascade system and a negative signal conversion mechanism, this invention achieves multi-cascade amplification of target recognition signals, significantly improving the sensitivity of pathogen detection and effectively capturing weak signals from extremely low copy number pathogens. Attached Figure Description

[0016] Figure 1 This is a system diagram of a highly sensitive biosensor-based pathogen detection system according to the present invention.

[0017] Figure 2 This is a system diagram of the data acquisition unit in a highly sensitive biosensor-based method for detecting pathogens according to the present invention.

[0018] Figure 3 This is a flowchart illustrating the signal acquisition, conversion, and data analysis and judgment process in a highly sensitive biosensor-based pathogen detection system of the present invention.

[0019] Explanation of icon numbers: 1. Micro-detection and preprocessing module; 11. Multi-layer gradient microporous filtration unit; 12. Magnetic bead targeted enrichment linkage unit; 13. Multi-channel parallel sampling unit; 2. High-sensitivity biosensing core module; 21. Specific bio-recognition unit; 22. Sensing transducer unit; 3. Microfluidic reaction and signal amplification module; 31. Microchannel fluid control unit; 32. DNA nanoscaffold loading unit; 33. Catalytic cascade reaction unit; 34. Negative signal conversion unit; 4. Low-noise signal acquisition and conversion module; 41. High-gain low-noise conditioning unit; 42. Filtering unit; 43. Analog-to-digital conversion unit; 44. Effective signal extraction unit; 5. Data analysis and micro-reading module; 51. Zero-point drift correction unit; 52. Nonlinear fitting calculation unit; 53. Extremely low copy threshold determination unit; 54. Quantitative result output unit. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: like Figure 1 and Figure 3 As shown, this embodiment provides a highly sensitive biosensor-based pathogen detection system, including a trace detection and preprocessing module 1, a highly sensitive biosensing core module 2, a microfluidic reaction and signal amplification module 3, a low-noise signal acquisition and conversion module 4, and a data analysis and trace interpretation module 5. Specifically: the trace detection and preprocessing module 1 is used to integrate a multi-layer gradient microporous filtration and magnetic bead targeted enrichment linkage structure with a multi-channel parallel sampling pipeline for layered impurity removal, targeted pathogen enrichment, and simultaneous preprocessing of multiple samples in complex matrices; the highly sensitive biosensing core module 2 is used to convert the weak biological response signal generated by the specific capture of pathogens into a detectable physical signal based on specific biorecognition elements and sensing transducers. Signal; Microfluidic reaction and signal amplification module 3: used to regulate the reaction fluid through a microchannel network and construct a catalytic cascade reaction system using DNA nanoscaffolds loaded with nanozymes. Through the negative signal conversion mechanism of enzyme catalytic products synergistically decomposing chromogenic substrates, multiple cascade amplifications of target recognition signals are performed in a small volume; Low-noise signal acquisition and conversion module 4: used to perform high-gain, low-noise conditioning, filtering, and analog-to-digital conversion on the weak analog signals output by the sensor, and extract the effective detection signals; Data analysis and micro-quantity interpretation module 5: based on the built-in analysis algorithm, performs zero-point drift correction and nonlinear fitting calculation on the detection signal, determines the concentration threshold for extremely low copy number pathogens, and outputs the quantitative detection results of pathogens.

[0022] It should be noted that the trace detection and preprocessing module 1 completes the layered impurity removal and targeted enrichment of pathogens, and then enters the high-sensitivity biosensing core module 2 for specific identification and converts the weak biological response into a physical signal. This signal is then amplified in the microfluidic reaction and signal amplification module 3 through a catalytic cascade system of DNA nanoscaffold-loaded nanozymes to achieve multiple negative cascade amplification. The amplified weak analog signal is then subjected to high-gain conditioning, filtering and noise reduction, and analog-to-digital conversion by the low-noise signal acquisition and conversion module 4 to extract effective feature signals. Finally, it is transmitted to the data analysis and trace interpretation module 5 for zero-point drift correction, nonlinear fitting calculation, and threshold determination, thereby outputting accurate quantitative detection results of pathogens.

[0023] In this embodiment, it should also be noted that the trace detection and pretreatment module 1 includes a multi-layer gradient microporous filtration unit 11, a magnetic bead targeted enrichment linkage unit 12, and a multi-channel parallel sampling unit 13. Specifically: the multi-layer gradient microporous filtration unit 11 is used to physically grade and filter complex matrix samples through a multi-layer filter membrane with decreasing pore size; the multi-layer filter membrane includes at least a large-pore pre-filtration layer, a medium-pore interception layer, and a small-pore fine filtration layer arranged sequentially along the sample flow direction; wherein the pore size of the large-pore pre-filtration layer is 5-50 μm, the pore size of the medium-pore interception layer is 0.1-5 μm, and the pore size of the small-pore fine filtration layer is 0.01-0.1 μm. The magnetic bead targeted enrichment linkage unit 12 is used to efficiently capture, separate, and enrich target pathogens under magnetic field control using surface-modified magnetic beads that specifically recognize molecules; the multi-channel parallel sampling unit 13 is used to integrate multiple independent sampling pipelines for simultaneous aspiration and parallel pretreatment of multiple samples.

[0024] It should be noted that the multi-channel parallel sampling unit 13 first simultaneously absorbs and transports multiple complex matrix samples in parallel. Then, the samples flow sequentially through the multi-layer filter membrane with decreasing pore size in the multi-layer gradient microporous filtration unit 11. Physical graded filtration and impurity removal are completed through the large-pore pre-filtration layer, the medium-pore interception layer and the small-pore fine filtration layer. Finally, the magnetic beads with specific recognition molecules modified on the surface of the magnetic bead targeted enrichment linkage unit 12 are used to efficiently capture, separate and enrich the target pathogens in the filtered samples under the control of the magnetic field.

[0025] Furthermore, it should be noted that in the multi-layer gradient microporous filtration unit 11, the large-pore pre-filtration layer uses a glass fiber membrane, the medium-pore interception layer uses a mixed cellulose membrane, and the small-pore fine filtration layer uses a hydrophilic polytetrafluoroethylene membrane. The three-layer gradient membrane progressively intercepts large particulate suspended solids, colloidal impurities, and tiny organic debris, allowing only target pathogens to pass through the membrane to the next stage, achieving selective filtration with graded interception and pathogen permeability.

[0026] After capturing, separating, and enriching the target pathogens, the magnetic bead targeted enrichment linkage unit 12 also includes the following operations: C1: Magnetic bead washing: Using an external magnetic field, magnetic beads containing the target bacteria are fixed at the bottom of the reaction chamber. The supernatant is removed, and 1×PBS buffer is added to wash repeatedly 2-3 times to remove non-specific adsorbed impurities that have not been bound. C2: Target elution: Add 50-100 μL of elution buffer (0.1 M glycine-HCl, pH 2.5) to the washed magnetic bead complex and incubate for 3-5 minutes to allow the target bacteria to dissociate from the surface of the magnetic beads, obtaining a high-purity, high-concentration test bacterial solution.

[0027] The multi-channel parallel sampling unit 13 has an independent parallel microfluidic pipeline structure, which can support simultaneous sampling of 3 channels / 6 channels / 8 channels. It can complete the simultaneous sampling of multiple complex matrix samples (water, food leachate, clinical body fluid) from different sources in a single run.

[0028] In this embodiment, it should also be noted that the high-sensitivity biosensing core module 2 includes a specific bio-recognition unit 21 and a sensing transducer unit 22, wherein: the specific bio-recognition unit 21 is used to fix the recognition elements of antibodies, aptamers, and receptors, specifically capture target pathogens and generate biological response signals; the sensing transducer unit 22 is used to convert the weak biological signals generated by the capture of pathogens into measurable physical signals.

[0029] It should be noted that the target pathogen is first accurately identified and specifically captured by a specific biorecognition unit 21 that has fixed recognition elements such as antibodies, aptamers or receptors, thereby generating a weak biological response signal. This biological signal is then received by the sensing transducer unit 22 and converted into a physical signal that can be read and processed by the subsequent system.

[0030] Furthermore, it should be noted that the specific biometric unit 21 employs a self-assembled monolayer covalent fixation process to firmly modify the antibody, aptamer, and receptor onto the surface of the sensing electrode, avoiding detachment and non-specific adsorption.

[0031] The sensing transducer unit 22 uses an electrochemical sensing electrode as the transducer substrate to convert the potential, current and impedance changes caused by bioaffinity into measurable electrophysical signals.

[0032] In this embodiment, it should also be noted that the microfluidic reaction and signal amplification module 3 includes a microchannel fluid control unit 31, a DNA nanoscaffold loading unit 32, a catalytic cascade reaction unit 33, and a negative signal conversion unit 34. Specifically: the microchannel fluid control unit 31 is used to precisely control the flow path, mixing, and residence time of the reaction fluid by designing a micrometer-scale channel network; the DNA nanoscaffold loading unit 32 is used to orderly load nanozymes onto DNA self-assembled nanostructures to achieve localized high-concentration catalytic sites. The specific operation is as follows: A1: Single-stranded DNA is used to self-assemble into tetrahedral DNA nanoscaffolds through base complementary pairing, and amino active sites are modified on the surface of the tetrahedral DNA nanoscaffolds; A2: The nanozyme is dispersed in a buffer solution, and the nanozyme is orderly anchored onto the active sites of the tetrahedral DNA nanoscaffold through amino-carboxyl covalent bonding; A3: The reaction temperature is controlled at 37°C and the reaction time is 2-3 hours to achieve high-density, orderly loading of the nanozyme on the DNA nanoscaffold, forming localized high-concentration catalytic sites. Catalytic cascade reaction unit 33: Used to construct a multi-step enzyme catalytic reaction chain, so that the product of the previous step becomes the substrate of the next step for step-by-step signal amplification; the specific operation is as follows: B1: The nanozyme supported on the DNA nanoscaffold acts as the first-stage catalyst, catalyzing the oxidation of glucose to generate hydrogen peroxide and lowering the pH of the reaction system; B2: The generated hydrogen peroxide acts as the oxidant of the second-stage reaction under acidic conditions, synergistically catalyzing the decomposition of the chromogenic substrate with characteristic absorption peaks. Nanosheets, transforming them into colorless B3: As the concentration of the target pathogen increases, the chromogenic substrate... The nanosheets are largely decomposed, leading to a significant decrease in the absorbance or electrical signal intensity of the detection system, resulting in negative signal conversion and stepwise amplification due to substrate consumption. Negative signal conversion unit 34: This unit is used to establish a negative correlation between target concentration and detection signal intensity by synergistically decomposing the chromogenic substrate through enzyme catalysis.

[0033] It should be noted that the reaction fluid first undergoes precise control of its flow path, mixing, and residence time via a micron-scale channel network in the microchannel fluid control unit 31. Simultaneously, the DNA nanoscaffold loading unit 32, prepared by reacting at 37°C for 2-3 hours, participates in the reaction as a core catalyst. In the catalytic cascade reaction unit 33, the loaded nanozyme first catalyzes the oxidation of glucose to hydrogen peroxide and lowers the pH of the system. Subsequently, the generated hydrogen peroxide synergistically catalyzes the decomposition of the chromogenic substrate under acidic conditions. Nanosheets transform it into a colorless form. Finally, the negative signal conversion unit 34 amplifies the negative correlation signal step by step, which is caused by the significant decrease in absorbance or electrical signal intensity of the detection system as the concentration of the target pathogen increases and the chromogenic substrate is consumed in large quantities.

[0034] Furthermore, it should be noted that the signal amplification factor A of the negative signal conversion unit 34 is determined by the colorimetric substrate. The decomposition rate of the nanosheets, and its negative correlation with the concentration C of the target pathogen, can be characterized by the following formula: ; in, S represents the initial electrical signal intensity, S represents the signal intensity after the reaction, k represents the reaction rate constant, and t represents the reaction time. As the concentration of the target pathogen C increases, the signal attenuation increases exponentially, thereby achieving highly sensitive negative quantitative detection.

[0035] In this embodiment, it should also be noted that the low-noise signal acquisition and conversion module 4 includes a high-gain low-noise conditioning unit 41, a filtering unit 42, an analog-to-digital conversion unit 43, and an effective signal extraction unit 44, wherein: the high-gain low-noise conditioning unit 41 is used to pre-amplify and suppress common-mode noise of the weak analog signal output by the sensor, thereby improving the signal-to-noise ratio; the filtering unit 42 is used to filter out high-frequency noise and interference frequency components in the signal through a low-pass or band-pass filter; the analog-to-digital conversion unit 43 is used to convert the conditioned and filtered continuous analog signal into a discrete digital signal; and the effective signal extraction unit 44 is used to identify and extract characteristic signal components related to the concentration of pathogens from the digital signal.

[0036] It should be noted that the weak analog signal output by the sensor is first amplified and common-mode noise suppressed by the high-gain low-noise conditioning unit 41 to improve the signal-to-noise ratio. Then, the high-frequency noise and interference frequency components in the signal are filtered out by the filtering unit 42 through low-pass or band-pass filtering. Next, the conditioning and filtering continuous analog signal is converted into a discrete digital signal by the analog-to-digital conversion unit 43. Finally, the effective signal extraction unit 44 accurately identifies and extracts the feature signal components that are highly correlated with the concentration of pathogens from the digital signal.

[0037] Furthermore, it should be noted that the filter type in filter unit 42 is a combination of a second-order low-pass filter and a power frequency notch filter, specifically designed to filter out 50Hz / 60Hz power frequency interference and high-frequency noise.

[0038] In this embodiment, it should also be noted that the data analysis and trace interpretation module 5 includes a zero-point drift correction unit 51, a nonlinear fitting operation unit 52, an extremely low copy threshold determination unit 53, and a quantitative result output unit 54, wherein: the zero-point drift correction unit 51: eliminates signal zero-point drift caused by the environment based on real-time monitoring of the baseline signal and algorithm compensation; the nonlinear fitting operation unit 52: is used to perform nonlinear regression fitting on the signal-concentration relationship using a standard curve; the extremely low copy threshold determination unit 53: is used to classify and determine whether the weak signal is higher than the background noise according to the preset confidence interval and detection limit; the quantitative result output unit 54: is used to convert the determination result into a pathogen concentration value and output it through a display or data interface.

[0039] It should be noted that the acquired digital signal is first monitored and compensated by the zero-point drift correction unit 51 based on the baseline signal in real time to eliminate the zero-point drift caused by environmental factors. Then, the nonlinear fitting operation unit 52 uses a standard curve to perform nonlinear regression fitting on the signal-concentration relationship. Next, the extremely low copy threshold determination unit 53 determines whether the weak signal is higher than the background noise according to the preset confidence interval and detection limit. Finally, the quantitative result output unit 54 converts the determination result into a specific pathogen concentration value and outputs the final quantitative detection result through display or data interface.

[0040] Furthermore, it should be noted that the nonlinear fitting unit 52 uses a four-parameter logistic model to perform regression fitting on the signal-concentration relationship, and its model formula is as follows: ; Where S(C) is the detection signal corresponding to the target pathogen concentration C. and These are the minimum and maximum signal responses, respectively. denoted as half-inhibition concentration (the bacterial concentration at which the signal decreases by 50%), and H as the Hill slope. This model is suitable for describing the substrate-consuming nonlinear response curve in the negative signal conversion unit 34, enabling accurate quantification of bacteria with extremely low copy numbers.

[0041] The hierarchical determination of the extremely low copy threshold determination unit 53 specifically includes: D1: Background noise measurement: In a blank control sample without the target pathogen, the measurement was repeated ≥10 times, and the average background signal was calculated. and standard deviation ; D2: Detection Limit Setting: Set the detection limit (LOD) to... Set the limit of quantitation (LOQ) to ; D3: Sample classification determination: When the sample detection signal S < LOD, it is determined as "negative"; when LOD ≤ S < LOQ, it is determined as "suspected positive / below the quantification limit"; when S ≥ LOQ, quantitative calculation is performed and it is determined as "positive".

[0042] Example 2: As Figure 2 shown, in this example, a highly sensitive detection method for pathogenic bacteria using a biosensor specifically includes the following steps: S1. Pretreatment of trace samples: S1.1: Synchronously aspirate and parallelly transport multiple complex matrix samples; S1.2: Subsequently, the sample flows through multiple filter membranes with gradually decreasing medium pore sizes in sequence, passing through a large pore pre-filter layer (glass fiber filter membrane) with a pore size of 5 - 50 μm, a medium pore interception layer (mixed cellulose filter membrane) with a pore size of 0.1 - 5 μm, and a small pore fine filter layer (hydrophilic polytetrafluoroethylene filter membrane) with a pore size of 0.01 - 0.1 μm in sequence to complete physical classification filtration and impurity removal; S1.3: The filtered sample enters and uses magnetic beads modified with specific recognition molecules on the surface to efficiently capture the target pathogenic bacteria under the control of a magnetic field. Subsequently, the magnetic beads are fixed by applying an external magnetic field, the supernatant is removed, 1×PBS buffer is added and washed repeatedly 2 - 3 times, and then 50 - 100 μL of elution buffer (0.1M glycine - HCl, pH 2.5) is added and incubated for 3 - 5 minutes to dissociate the target pathogenic bacteria from the surface of the magnetic beads, obtaining a high - purity and high - concentration test bacterial solution; S2. Biological recognition and signal conversion: S2.1: The test bacterial solution obtained in step S1 is precisely recognized and specifically captured by fixed antibody, aptamer or receptor recognition elements for the target pathogenic bacteria, generating a weak biological response signal; S2.2: Subsequently, this signal is received by an electrochemical sensing electrode, converting the potential, current or impedance change caused by biological affinity into a measurable electro - physical signal; S3. Multiple - stage cascade signal amplification: S3.1: The reaction fluid first undergoes precise control of the flow path, mixing and residence time through a micron - scale channel network; S3.2: Tetrahedral DNA nanoscaffolds prepared in advance (with amino - active sites modified on the surface, orderly loading nano - enzymes through amino - carboxyl covalent binding, and reacting at 37 °C for 2 - 3 hours to form local high - concentration catalytic sites) participate in the reaction as the core catalyst; S3.3: The loaded nano - enzyme first catalyzes the oxidation of glucose to generate hydrogen peroxide and reduces the pH value of the system. Subsequently, the generated hydrogen peroxide synergistically catalyzes the decomposition of the chromogenic substrate nano - sheets in an acidic environment, converting them into colorless ; S3.4: The negative correlation signal that finally decreases significantly in the absorbance or electrical signal intensity of the detection system as the target pathogen concentration increases and the chromogenic substrate is consumed in large quantities is amplified step by step; the signal amplification factor A is characterized by the formula , where is the initial signal intensity, S is the signal intensity after the reaction, k is the reaction rate constant, C is the target pathogen concentration, and t is the reaction time; S4. Signal acquisition and conversion: S4.1: The weak analog signal amplified in step S3 is amplified and the noise is reduced by the preamplifier and common-mode noise suppression circuit to improve the signal-to-noise ratio; S4.2: Subsequently, the 50 Hz / 60 Hz power frequency interference and high-frequency clutter are filtered out by the combination of a second-order low-pass filter and a power frequency notch filter; S4.3: Then, the continuous analog signal after conditioning and filtering is converted into a discrete digital signal, and finally, the characteristic signal component highly correlated with the pathogen concentration is accurately identified and extracted from the digitalized signal; S5. Data analysis and interpretation: S5.1: The characteristic signal extracted in step S4 is first monitored in real time based on the baseline signal and compensated by an algorithm to eliminate the signal zero drift caused by environmental factors; S5.2: Subsequently, a four-parameter logistic model is used to perform non-linear regression fitting on the signal-concentration relationship, and the model formula is: ; where S(C) is the detection signal corresponding to the target pathogen concentration C, and are the minimum and maximum signal responses respectively, is the half-inhibition concentration (the pathogen concentration when the signal drops by 50%), and H is the Hill slope; S5.3: Then, classification determination is performed according to the preset confidence interval and detection limit: The blank control sample without the target pathogen is measured repeatedly ≥10 times, and the average value and the standard deviation of the background signal are calculated. The detection limit LOD is set to , and the quantification limit LOQ is set to ; When the sample detection signal S < LOD, it is determined as "negative", when LOD ≤ S < LOQ, it is determined as "suspected positive / below the quantification limit", and when S ≥ LOQ, quantitative calculation is performed and it is determined as "positive"; S5.4: Finally, the determination result is converted into a specific pathogen concentration value, and the final quantitative detection result is output through display or data interface.

[0043] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0044] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A highly sensitive biosensor-based pathogen detection system, characterized in that, It includes a trace detection and preprocessing module (1), a high-sensitivity biosensing core module (2), a microfluidic reaction and signal amplification module (3), a low-noise signal acquisition and conversion module (4), and a data analysis and trace interpretation module (5), wherein: The trace detection and preprocessing module (1) is used to carry a multi-layer gradient microporous filtration and magnetic bead targeted enrichment linkage structure and integrate a multi-channel parallel sampling pipeline to perform layered impurity removal, targeted enrichment of pathogens and simultaneous preprocessing of multiple samples in complex matrix trace samples. The highly sensitive biosensing core module (2) is used to convert the weak biological response signal generated by the specific capture of pathogens into a detectable physical signal based on the specific bio-recognition element and the sensing transducer. The microfluidic reaction and signal amplification module (3) is used to regulate the reaction fluid through a microchannel network and to construct a catalytic cascade reaction system by loading nanozymes with DNA nanoscaffolds. Through the negative signal conversion mechanism of enzyme catalytic products synergistically decomposing chromogenic substrates, multiple cascade amplification of target recognition signals is performed in a small volume. The low-noise signal acquisition and conversion module (4) is used to perform high-gain, low-noise conditioning, filtering and analog-to-digital conversion on the weak analog signal output by the sensor, and to extract the effective detection signal. The data analysis and trace interpretation module (5) performs zero-point drift correction and nonlinear fitting calculation on the detection signal based on the built-in analysis algorithm, determines the concentration threshold for bacteria with extremely low copy number, and outputs the quantitative detection results of bacteria.

2. The biosensor-based high-sensitivity pathogen detection system according to claim 1, characterized in that, The trace detection and preprocessing module (1) includes a multi-layer gradient microporous filtration unit (11), a magnetic bead targeted enrichment linkage unit (12), and a multi-channel parallel sampling unit (13), wherein: The multi-layer gradient microporous filtration unit (11) is used to perform physical hierarchical filtration of complex matrix samples through a multi-layer filter membrane with decreasing pore size. The magnetic bead targeted enrichment linkage unit (12) is used to efficiently capture, separate and enrich target pathogens under the control of a magnetic field by using magnetic beads that specifically recognize molecules through surface modification. The multi-channel parallel sampling unit (13) is used to integrate multiple independent sampling pipelines to simultaneously absorb and preprocess multiple samples.

3. The biosensor-based high-sensitivity pathogen detection system according to claim 2, characterized in that, The multilayer gradient microporous filtration unit (11) includes at least a large-pore pre-filtration layer, a medium-pore interception layer, and a small-pore fine filtration layer arranged sequentially along the sample flow direction; wherein the pore size of the large-pore pre-filtration layer is 5-50 μm, the pore size of the medium-pore interception layer is 0.1-5 μm, and the pore size of the small-pore fine filtration layer is 0.01-0.1 μm.

4. The biosensor-based high-sensitivity pathogen detection system according to claim 1, characterized in that, The highly sensitive biosensing core module (2) includes a specific biometric identification unit (21) and a sensing transducer unit (22), wherein: The specific biometric unit (21) is used to fix the recognition element of antibodies, aptamers, and receptors, specifically capture target pathogens and generate biological response signals; The sensing transducer unit (22) is used to convert the weak biological signals generated by pathogen capture into measurable physical signals.

5. The biosensor-based high-sensitivity pathogen detection system according to claim 1, characterized in that, The microfluidic reaction and signal amplification module (3) includes a microchannel fluid control unit (31), a DNA nanoscaffold loading unit (32), a catalytic cascade reaction unit (33), and a negative signal conversion unit (34), wherein: The microchannel fluid control unit (31) is used to precisely control the flow path, mixing and residence time of the reaction fluid by designing a micron-level channel network. The DNA nanoscaffold loading unit (32) is used to load nanozymes in an orderly manner using DNA self-assembled nanostructures as carriers to achieve local high-concentration catalytic sites. The catalytic cascade reaction unit (33) is used to construct a multi-step enzyme catalytic reaction chain, so that the product of the previous stage becomes the substrate of the next stage for signal amplification step by step. The negative signal conversion unit (34) is used to establish a negative correlation between the target concentration and the detection signal intensity by co-decomposing the chromogenic substrate through enzyme catalysis products.

6. The biosensor-based high-sensitivity pathogen detection system according to claim 5, characterized in that, The DNA nanoscaffold loading unit (32) uses a DNA self-assembled nanostructure as a carrier to orderly load nanozymes for local high-concentration catalytic sites. The specific operation is as follows: A1: Tetrahedral DNA nanoscaffolds are formed by self-assembly of single-stranded DNA through base complementary pairing, and the surface of the tetrahedral DNA nanoscaffolds is modified with amino active sites. A2: The nanozyme is dispersed in a buffer solution and then anchored to the active site of a tetrahedral DNA nanoscaffold through amino-carboxyl covalent bonding. A3: By adjusting the reaction temperature to 37℃ and the reaction time to 2-3h, nanozymes are loaded onto DNA nanoscaffolds in a high-density and orderly manner, forming local high-concentration catalytic sites.

7. The biosensor-based high-sensitivity pathogen detection system according to claim 5, characterized in that, The catalytic cascade reaction unit (33) constructs a multi-step enzyme catalytic reaction chain, so that the product of the previous stage becomes the substrate of the next stage for stepwise signal amplification. The specific operation is as follows: B1: Nanozymes loaded on DNA nanoscaffolds act as first-stage catalysts, catalyzing the oxidation of glucose to hydrogen peroxide and lowering the pH of the reaction system. B2: The generated hydrogen peroxide acts as an oxidant in the second-order reaction under acidic conditions, co-catalyzing the decomposition of the chromogenic substrate with characteristic absorption peaks. Nanosheets, transforming them into colorless ; B3: As the concentration of the target pathogen increases, the chromogenic substrate... The nanosheets are decomposed in large quantities, resulting in a significant decrease in the absorbance or electrical signal intensity of the detection system, leading to negative signal conversion and stepwise amplification of substrate consumption.

8. The biosensor-based high-sensitivity pathogen detection system according to claim 1, characterized in that, The low-noise signal acquisition and conversion module (4) includes a high-gain low-noise conditioning unit (41), a filtering unit (42), an analog-to-digital conversion unit (43), and an effective signal extraction unit (44), wherein: The high-gain low-noise conditioning unit (41) is used to pre-amplify and suppress common-mode noise of the weak analog signal output by the sensor, thereby improving the signal-to-noise ratio. The filtering unit (42) is used to filter out high-frequency noise and interference frequency components in the signal through a low-pass or band-pass filter. The analog-to-digital conversion unit (43) is used to convert the conditioned and filtered continuous analog signal into a discrete digital signal. The effective signal extraction unit (44) is used to identify and extract characteristic signal components related to pathogen concentration from the digital signal.

9. The biosensor-based high-sensitivity pathogen detection system according to claim 1, characterized in that, The data analysis and trace interpretation module (5) includes a zero-point drift correction unit (51), a nonlinear fitting operation unit (52), an extremely low copy threshold determination unit (53), and a quantitative result output unit (54), wherein: The zero-point drift correction unit (51) eliminates signal zero-point drift caused by the environment based on real-time monitoring of the baseline signal and algorithm compensation. The nonlinear fitting operation unit (52) is used to perform nonlinear regression fitting on the signal-concentration relationship using a standard curve; The extremely low copy threshold determination unit (53) is used to classify whether the weak signal is higher than the background noise based on the preset signal interval and detection limit. The quantitative result output unit (54) is used to convert the judgment result into a pathogen concentration value and output it through a display or data interface.

10. A highly sensitive biosensor method for detecting pathogens, comprising a highly sensitive biosensor system for detecting pathogens according to claims 1-9, characterized in that, Includes the following steps: S1: Gradient gradation filtration, impurity removal and targeted enrichment of target pathogens with magnetic beads are performed on micro samples of complex multi-channel matrix to obtain high-purity samples of pathogens to be tested. S2: The target pathogen is captured by a specific recognition element and a biological response signal is generated. The weak biological response signal is then converted into a detectable physical signal by a sensor transducer. S3: Based on the precise control of fluid reaction conditions by microfluidic channels, a multi-level catalytic cascade system is constructed by loading nanozymes onto DNA nanoscaffolds, and the negative correlation between target concentration and detection signal is amplified step by step by utilizing the consumption of chromogenic substrates; S4: Perform high-gain low-noise conditioning, filtering and noise reduction, and analog-to-digital conversion on the weak analog signal output by the sensor, remove interference, and extract effective feature signals related to the concentration of pathogens. S5: Perform zero-point drift compensation and nonlinear fitting calculation on the acquired signal, determine the threshold of extremely low copy number pathogens according to the preset detection limit confidence interval, and output accurate quantitative detection results of pathogens.

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