Fingertip blood myocardial marker detection method and system based on dynamic baseline

Abstract

The invention relates to the technical field of biomedical detection, and discloses a fingertip blood myocardial marker detection method based on a dynamic baseline, which comprises the following steps: S1, sample pretreatment: collecting a fingertip blood sample by using a fingertip blood taking needle, and injecting the sample into a sample injection end of a micro-fluidic chip with a built-in gradient filtering channel; a gradient filtering channel of the micro-fluidic chip is used for separating red blood cells and impurities in a sample, and an anti-coagulation coating is preset on the inner wall of the chip; a sample is driven to flow through a gradient filtering channel through a built-in micropump of the chip, so that red blood cells are separated from impurities. The dynamic baseline calibration module is introduced to sense and compensate the influence of the change of environmental parameters such as temperature, humidity and air pressure on the electrical baseline of the system in real time, and the baseline drift is controlled at an extremely low level, so that the detection system can be kept in a stable working state under different altitudes and weather conditions; the problem that detection results of existing POCT equipment are not repeated and unreliable due to environmental fluctuation is fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, specifically to a method and system for detecting myocardial biomarkers in fingertip blood based on dynamic baseline. Background Technology

[0002] Rapid and accurate detection of myocardial markers (such as cTnI, CK-MB, and NT-proBNP) is crucial for the early diagnosis, risk stratification, and efficacy monitoring of acute cardiovascular diseases. Traditional laboratory testing methods, such as enzyme-linked immunosorbent assay (ELISA), usually require venous blood collection, sample centrifugation, multiple manual operations, and large-scale instrument analysis, resulting in long testing cycles and high requirements for the operating environment, making it difficult to meet the immediate needs of point-of-care testing (POCT).

[0003] In recent years, point-of-care testing (POCT) technology based on microfluidics and optical detection has provided a new approach for the rapid detection of myocardial biomarkers. However, existing technologies still have two significant shortcomings in practical applications:

[0004] First, the equipment has poor environmental adaptability and unstable detection baseline. The optical and electrical detection modules of existing POCT equipment are easily affected by fluctuations in ambient temperature, humidity and air pressure, which causes the signal baseline of the detection system to drift. This baseline drift will seriously interfere with the weak signal, especially of low-concentration target substances, resulting in inaccurate detection results and poor repeatability, which limits the reliable application of the equipment in non-standard laboratory environments such as homes, ambulances and primary clinics.

[0005] Second, sample pretreatment is cumbersome and has low integration with the detection system. For small whole blood samples such as finger prick blood, effective plasma / serum separation is a prerequisite for accurate quantification. Existing technologies mostly rely on external separation steps or simple filter membranes, which have problems such as low separation efficiency, easy clogging, easy hemolysis, or insufficient anticoagulation. They have failed to form an efficient, closed, and automated integrated process with subsequent biological specific recognition and signal detection modules, which affects the overall speed of detection and ease of operation. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for detecting myocardial biomarkers in fingertip blood based on dynamic baselines, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting myocardial biomarkers in fingertip blood based on dynamic baseline, comprising the following steps:

[0008] S1: Sample pretreatment: After collecting a fingertip blood sample using a fingertip lancet, the sample is injected into the sample inlet of a microfluidic chip with a built-in gradient filtration channel. The gradient filtration channel of the microfluidic chip is used to separate red blood cells and impurities in the sample, and the inner wall of the chip is pre-coated with an anticoagulant coating. The sample is driven to flow through the gradient filtration channel by a built-in micropump to achieve the separation of red blood cells and impurities. Combined with the anticoagulant coating, anticoagulant treatment is completed, and finally, the serum sample to be tested is collected from the sample outlet of the chip.

[0009] S2: Dynamic baseline calibration, which includes a baseline acquisition unit, an environmental parameter sensing unit, and a baseline dynamic correction unit. The baseline acquisition unit acquires the dark current signal of the optical detection unit under conditions of no sample and no excitation light source, and converts it into the initial electrical signal baseline of the detection system under no-load conditions through the signal acquisition circuit. The sampling point of the baseline acquisition unit is located on the signal path between the output of the photomultiplier tube and the pre-amplification module. The environmental parameter sensing unit acquires the temperature, humidity, and air pressure parameters of the detection environment in real time, and the baseline dynamic correction unit corrects the initial electrical signal baseline in real time according to the changes of the above parameters to obtain a dynamic calibration baseline.

[0010] S3: Specific detection: The pretreated serum sample to be tested is quantitatively injected into the reaction cell, and a preset amount of myocardial marker-specific antibody labeled with fluorescent probe is added simultaneously; the reaction cell has a built-in oscillating mixing structure, and after being mixed evenly by the oscillating mixing structure, it is incubated at a constant temperature of 37°C for a preset time to form a reaction complex; an optical detection module is set up to scan the reaction complex and collect the fluorescence signal it generates.

[0011] S4: Signal processing and result output. A signal processing unit and a result output unit are set up. Based on the dynamic calibration baseline obtained in step S2, background interference from the fluorescence signal is subtracted. The correction accuracy of the dynamic calibration baseline ensures that, under environmental parameter variations of ±10%, the system's detection coefficient of variation (CV) for the 0.1 ng / mL cTnI standard is ≤5%. After noise reduction and signal enhancement by the signal processing unit, the signal is compared with a preset standard curve for myocardial markers to complete the quantitative analysis of myocardial markers. The detection results are output through the result output unit.

[0012] As a preferred embodiment of the present invention, in step S1, the gradient filtration channel of the microfluidic chip is a three-layer modified nanofiber membrane stacked structure, with hydrophilic modified groups grafted onto the surface of each nanofiber membrane; the pore size decreases gradually from the sample inlet to the sample outlet at 8-10 μm, 2-5 μm, and 0.5-1 μm, respectively, specifically intercepting red blood cells, large-sized impurities, and small molecule impurities; the anticoagulant coating is a heparin-chitosan cross-linked composite coating, with a micro-nano uneven structure constructed on the coating surface to improve anticoagulant stability; The sample inlet, sample outlet, and inner wall of the connecting pipe of the microfluidic chip are all covered with the heparin-chitosan cross-linked composite anticoagulant coating. The gradient filtration channel is composed of three independent modified nanofiber membranes. Each membrane is bonded to a pre-reserved groove on the PDMS substrate after being activated by oxygen plasma. The membrane surface is not covered with an anticoagulant coating to avoid clogging the nanopores. The sample processing flow rate is controlled at 5-10 μL / min through a micropump closed loop. The chip temperature is maintained at 25-30℃ during the processing, and the processing time is 3-5 min.

[0013] As a preferred embodiment of the present invention, in step S2, the environmental parameter sensing unit and the baseline dynamic correction unit adopt a real-time linkage feedback mechanism. The environmental parameter sensing unit synchronously collects temperature, humidity, and air pressure parameters at a sampling frequency of 10Hz and performs multi-parameter fusion preprocessing. When the change in any environmental parameter exceeds a preset threshold, the baseline dynamic correction unit initiates a graded correction strategy, specifically: the parameter change amplitude is divided into three levels. For level one change, the correction response speed is set to 3-7ms / time, and the correction amplitude is 0.001-0.003V; for level two change, the correction response speed is set to 2-5ms / time, and the correction amplitude is 0.004-0.006V; for level three change, the correction response speed is set to 0.5-2ms / time, and the correction amplitude is 0.008-0.012V. The preset thresholds for temperature, humidity, and air pressure changes are ±0.5℃, ±5%RH, and ±5hPa, respectively, and the corrected dynamic calibration baseline must meet the requirement that the baseline drift is ≤0.01V.

[0014] As a preferred embodiment of the present invention, the myocardial markers in step S3 include cTnI, CK-MB, and NT-proBNP, and the corresponding specific antibodies are mixed in a 1:1:1 ratio, and each antibody is subjected to affinity purification treatment; the fluorescent probe is a time-resolved fluorescent microsphere with a surface-modified targeting group, and the targeting group is precisely matched with the specific binding site of the myocardial marker; the fluorescent microsphere has an excitation wavelength of 340 nm, an emission wavelength of 615 nm, and a fluorescence lifetime of ≥100 μs; the incubation process adopts a segmented isothermal strategy: first, it is incubated at 37℃ for 10 min, and then the temperature is increased to 40℃ for 5-10 min. The temperature fluctuation range of each stage of the segmented isothermal incubation is ≤±0.5℃, and the continuous oscillation of 50-80 r / min is maintained during the incubation process by the oscillating mixing structure built into the reaction cell.

[0015] As a preferred embodiment of the present invention, the signal processing unit in step S4 adopts a two-stage lock-in amplification structure, with a low-noise pre-amplification module in the front stage and a high-gain main amplification module in the rear stage. The amplification factor can be adjusted by 10 based on the fluorescence signal intensity. 3 -10 5 The standard curve is adaptively graded and adjusted; it is pre-constructed using a series of concentration gradients of myocardial biomarker standards, ranging from 0.01 to 100 ng / mL, with gradients set at 0.01, 0.1, 1, 10, 50, and 100 ng / mL, and the correlation coefficient R of the standard curve is [value missing]. 2 ≥0.995; During the quantitative analysis, a multi-point signal acquisition and screening structure is adopted. Specifically, 5-8 acquisition points are set for different regions of the same reaction complex. Each acquisition point is equipped with an independent signal acquisition branch and continuously acquires fluorescence signals 3 times. Abnormal signal values ​​are eliminated by the signal screening module using the three-times-standard-deviation (3σ) criterion: the mean μ and standard deviation σ of the three measurements at the same acquisition point are calculated. If any measurement value exceeds the range of [μ−3σ,μ+3σ], it is considered abnormal and eliminated. If all values ​​are valid, the mean is taken as the signal value of that point. The final fluorescence signal value is the median of the signal values ​​of all valid acquisition points.

[0016] The finger-prick blood myocardial biomarker detection system based on dynamic baseline includes a sample preprocessing module, a dynamic baseline calibration module, a specific detection module, a signal processing module, and a result output module. Each module is linked together sequentially along the detection process.

[0017] The sample pretreatment module includes a finger-prick blood collection component, a microfluidic chip, and a micropump. The collection component includes a finger-prick blood collection needle and a sample collection tube. The outlet of the sample collection tube is sealed to the inlet of the microfluidic chip via a sealing connector. The micropump is connected in series between the sample collection tube and the microfluidic chip to drive the sample to flow through the chip. The microfluidic chip integrates a gradient filtration channel and an anticoagulant coating, and the outlet is provided with a serum collection chamber.

[0018] The dynamic baseline calibration module includes a baseline acquisition unit, an environmental parameter sensing unit, and a baseline dynamic correction unit. The baseline acquisition unit is used to acquire the dark current signal of the optical detection unit under conditions of no sample and no excitation light source, and converts it into the initial electrical signal baseline of the detection system under no-load conditions through the signal acquisition circuit. The sampling point of the baseline acquisition unit is located on the signal path between the output terminal of the photomultiplier tube and the front-end low-noise pre-amplification module. The environmental parameter sensing unit is used to capture the temperature, humidity, and air pressure parameters of the detection environment. The baseline dynamic correction unit is connected to the former two respectively, and corrects the initial electrical signal baseline according to the parameter changes to obtain the dynamic calibration baseline.

[0019] The specific detection module includes a reaction cell, a temperature control unit, and an optical detection unit. The reaction cell is connected to the chip sample outlet via a pipe and has a built-in oscillating mixing structure. It is externally wrapped with a temperature control unit to maintain a temperature of 37°C. The optical detection unit includes an excitation light source, a filter assembly, and a signal acquisition element to acquire fluorescence signals.

[0020] The signal processing module is connected to the dynamic baseline calibration module and the specific detection module respectively. It includes a baseline subtraction unit, a noise reduction unit and a signal amplification unit. It is used to perform baseline subtraction, noise reduction and signal enhancement processing on the fluorescence signal in sequence, and then compare it with the preset standard curve to complete the quantitative analysis of myocardial markers.

[0021] The results output module connects to the signal processing module, displaying the analysis results in digital and curve form, and has data storage and export functions.

[0022] As a preferred embodiment of the present invention, the microfluidic chip is integrally formed by soft photolithography using PDMS material; the nanofiber membrane is prepared by electrospinning with a thickness of 20-30μm; the anti-coating coating is formed by layer-by-layer self-assembly with a thickness of 50-100nm, covering the sample inlet end, sample outlet end and inner wall of the connecting pipe outside the filter channel, but not covering the surface of the nanofiber membrane inside the gradient filter channel.

[0023] As a preferred embodiment of the present invention, the environmental parameter sensing unit includes a temperature sensor, a humidity sensor, and a barometric pressure sensor, all of which are distributed within the detection cavity of the detection system; the temperature sensor has a measurement accuracy of ±0.1℃, the humidity sensor ±2%RH, and the barometric pressure sensor ±1hPa, and the sampling frequency of each sensor is 10Hz; the baseline dynamic correction unit uses an FPGA chip as its core processing unit.

[0024] As a preferred embodiment of the present invention, the optical detection unit includes a pulsed LED excitation source, a filter group, a photomultiplier tube, and a signal acquisition circuit; the excitation filter has a center wavelength of 340nm and a half-bandwidth of 10nm, the emission filter has a center wavelength of 615nm and a half-bandwidth of 15nm; and the photomultiplier tube has a response time ≤1ns.

[0025] As a preferred embodiment of the present invention, the system further includes a power supply module, a wireless communication module, and a signal warning output module. The power supply module is a 2000mAh rechargeable lithium battery that supports stable 5V / 2A output and provides matching power to each module via a power management circuit. The wireless communication module uses the Bluetooth 5.0 protocol and is electrically connected to the signal processing module to achieve real-time transmission of detection data. The signal warning output module is connected to the wireless communication module, has pre-stored normal reference thresholds for different myocardial markers, and has built-in audible and visual warning components and a signal storage unit. When the detection value exceeds the corresponding preset threshold, it automatically issues a warning through sound and a pop-up window, while simultaneously enabling historical storage and export of detection data.

[0026] As a preferred technical solution of the present invention,

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. By introducing a dynamic baseline calibration module, the system can sense and compensate for the impact of changes in environmental parameters such as temperature, humidity, and air pressure on the electrical baseline of the system in real time, keeping the baseline drift at an extremely low level. This ensures that the detection system can maintain a stable working state under different altitudes and climate conditions, fundamentally solving the problem of non-repeatable and unreliable test results caused by environmental fluctuations in existing POCT equipment.

[0029] 2. A dedicated microfluidic chip with built-in gradient filtration channels and anticoagulant coating can rapidly separate high-quality serum from a small amount of fingertip blood within minutes and complete the anticoagulant treatment. This pretreatment module is seamlessly linked with the subsequent reaction and detection modules, forming an integrated closed detection process of "sample in - result out". This not only significantly shortens the total detection time and achieves truly rapid detection, but also simplifies the operation steps and reduces the technical requirements for operators and the dependence on the laboratory environment.

[0030] 3. By employing time-resolved fluorescence detection technology combined with a two-stage lock-in signal processing structure, the specific fluorescence signal is effectively amplified and background noise is suppressed. At the same time, the optimized segmented isothermal incubation strategy improves the antigen-antibody binding efficiency and specificity, and the multi-point signal acquisition and screening strategy further eliminates random errors caused by uneven local reactions. These measures work synergistically to enable the system to have excellent detection capability and accurate quantitative performance for low-concentration myocardial biomarkers.

[0031] 4. The system integrates wireless communication, local audible and visual early warning, data storage and export functions, enabling the test results to be displayed instantly, transmitted remotely, and actively alarmed in case of abnormalities. With built-in power management, the whole system is easy to carry and use on site, perfectly meeting the clinical and home application needs of rapid screening of cardiovascular diseases, pre-hospital emergency care and long-term monitoring. It is an effective supplement and extension to the central laboratory testing mode. Attached Figure Description

[0032] Figure 1 This is an overall flowchart of the finger-prick blood myocardial biomarker detection method based on dynamic baseline of the present invention;

[0033] Figure 2 This is a schematic diagram of the workflow of the dynamic baseline calibration module of the present invention, wherein the dashed box represents the built-in environmental parameter-baseline drift mapping table query and correction logic;

[0034] Figure 3 This is a schematic diagram of the arrangement of the reaction cell and optical detection unit in the specific detection module of the present invention. The diagram shows the baseline acquisition point position between the output end of the photomultiplier tube and the pre-amplification module. Detailed Implementation

[0035] 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.

[0036] Example 1: Implementation of the basic procedure for detecting myocardial biomarkers in fingertip blood based on dynamic baseline

[0037] This embodiment uses the cardiovascular outpatient department of a large tertiary hospital in China as an application scenario to illustrate the implementation process of the detection method of the present invention. The detection subjects are suspected patients with acute myocardial infarction, and a total of 30 fingertip blood samples are collected for testing.

[0038] S1: Sample Preprocessing

[0039] Approximately 50 μL of blood was collected from the subject's fingertip using a disposable sterile fingertip lancet (brand: BD, model: 366594) and immediately injected into the inlet of a microfluidic chip (made of PDMS material, integrally molded by soft photolithography). This chip integrates a gradient filtration channel composed of three layers of modified nanofiber membranes. Each membrane layer is grafted with hydrophilic modified polyvinyl alcohol (PVA) groups, and the pore sizes from the inlet to the outlet are 10 μm, 5 μm, and 1 μm, respectively. The inlet, outlet, and inner walls of the connecting channels of the microfluidic chip are all coated with a heparin-chitosan cross-linked composite. The gradient filtration channel consists of three independent modified nanofiber membranes. Each membrane is activated by oxygen plasma and bonded to a pre-reserved groove on the PDMS substrate. The membrane surface is not covered with an anticoagulant coating to avoid clogging the nanopores. The entire chip is placed in a constant temperature module to maintain 28°C. The sample is driven through the filtration channel at a flow rate of 8 μL / min by a built-in micro plunger pump (brand: Cole-Parmer, model: EW-07523-80). After processing for 5 minutes, approximately 30 μL of clear serum sample is collected from the sample outlet.

[0040] S2: Dynamic baseline calibration

[0041] After the detection system is started and before the sample is injected, the dynamic baseline calibration module is activated. The baseline acquisition unit (high-precision voltage acquisition card, brand: National Instruments, model: NI-9239) acquires the dark current signal of the optical detection unit under the condition of no sample and no excitation light source, and converts it into the initial electrical signal baseline of the detection system under no-load condition through the signal acquisition circuit. The sampling point of the baseline acquisition unit is located on the signal path between the output of the photomultiplier tube and the front-end low-noise pre-amplification module. The temperature sensor (model: DS18B20, accuracy ±0.1℃), humidity sensor (model: SHT31, accuracy ±2%RH), and barometric pressure sensor (model: BMP280, accuracy ±1hPa) in the environmental parameter sensing unit synchronously acquire environmental parameters within the detection chamber at a frequency of 10Hz. The baseline dynamic correction unit (based on a Xilinx Spartan-6 series FPGA) receives these parameters. The baseline dynamic correction unit has a built-in environmental parameter-baseline drift mapping table, which is pre-calibrated as follows: Under a standard laboratory environment, the temperature, humidity, and barometric pressure parameters are independently adjusted within their respective operating ranges in steps of 0.1℃, 1%RH, and 1hPa, respectively. The baseline voltage offset under no-load conditions at each step size is recorded. The offset data is fitted and stored in the mapping table. In actual testing, the baseline dynamic correction unit queries the mapping table to obtain the corresponding baseline correction amount based on the real-time parameters input from the environmental parameter sensing unit and performs the correction according to the preset response speed.

[0042] When any parameter change exceeds a preset threshold (temperature ±0.5℃, humidity ±5%RH, air pressure ±5hPa), a graded correction is initiated: if the change is level one (e.g., temperature change of 0.6℃), the baseline is corrected with a response speed of 5ms / time and an amplitude of 0.002V; if it is level two (e.g., humidity change of 8%RH), the correction is performed with a frequency of 3ms / time and an amplitude of 0.005V; if it is level three (e.g., air pressure change of 8hPa), the correction is performed with a frequency of 1ms / time and an amplitude of 0.01V. The corrected dynamic calibration baseline drift is ≤0.01V, and the correction accuracy of this dynamic calibration baseline ensures that, under environmental parameter changes of ±10%, the detection coefficient of variation (CV) of the system for 0.1ng / mL cTnI standard is ≤5%, and it is stored for later use.

[0043] S3: Specific Detection

[0044] 20 μL of pretreated serum sample was injected into a polycarbonate reaction chamber, along with 20 μL of a myocardial marker-specific antibody mixture labeled with fluorescent probes. This mixture contained an equal proportion of affinity-purified anti-cTnI, anti-CK-MB, and anti-NT-proBNP monoclonal antibodies (purchased from Abcam, catalog numbers ab47003, ab131048, and ab274399, respectively). Each antibody was covalently coupled to time-resolved fluorescent microspheres with surface-modified targeting groups (excitation wavelength 340 nm, emission wavelength 615 nm, fluorescence lifetime ≥100 μs, brand: Thermo Fisher, catalog number: F10720). The reaction chamber contained a micro magnetic stirrer and was placed in a temperature control unit. It was first incubated at 37°C and 250 rpm for 10 min, then heated to 40°C and incubated for another 8 min. The temperature fluctuation was ≤±0.5°C throughout the process. After incubation, an antibody-antigen-fluorescent microsphere reaction complex was formed.

[0045] S4: Signal Processing and Output

[0046] An optical detection module (including a pulsed LED excitation source, 340 / 10nm excitation filters, 615 / 15nm emission filters, and a photomultiplier tube (brand: Hamamatsu, model: H10721-01)) was used to scan the reaction cell and acquire fluorescence signals. The signal processing unit employs a two-stage lock-in amplification structure (pre-amplification module: ADI AD8429; main amplification module: TI OPA211), automatically selecting the amplification factor (10⁻⁶) based on the signal strength. 3 ~10 5First, the background fluorescence is subtracted from the dynamic calibration baseline obtained by S2, and then digital filtering is used for noise reduction. During the quantitative analysis, a multi-point signal acquisition and screening structure is adopted. Specifically, six acquisition points are set for different regions of the same reaction complex. Each acquisition point is equipped with an independent signal acquisition branch and continuously acquires fluorescence signals three times. Abnormal signal values ​​are eliminated by the signal screening module using the three-times-standard-deviation (3σ) criterion: the mean μ and standard deviation σ of the three measurements at the same acquisition point are calculated. If any measurement value exceeds the range of [μ−3σ,μ+3σ], it is considered abnormal and eliminated. If all values ​​are valid, their mean is taken as the signal value of that point. The final fluorescence signal value is the median of the signal values ​​of all valid acquisition points.

[0047] The processed fluorescence signal was compared with a pre-constructed standard curve (concentration gradient: 0.01, 0.1, 1, 10, 50, 100 ng / mL, R 2 By comparing the concentrations of cTnI, CK-MB, and NT-proBNP with a value of 0.998, quantitative analysis was performed to determine the concentrations of cTnI, CK-MB, and NT-proBNP. The results were displayed on an LCD screen in the form of numbers and trend curves, and the data could be exported via a USB interface.

[0048] This embodiment successfully achieved simultaneous and rapid testing of 30 clinical samples, with the entire testing process for a single sample taking approximately 25 minutes.

[0049] Example 2: Microfluidic chip structure optimization and anticoagulation performance verification

[0050] Based on Example 1, this embodiment focuses on verifying the filtration efficiency and anticoagulation stability of the microfluidic chip, using a medical device testing center in Shanghai as the testing platform.

[0051] Microfluidic chip fabrication:

[0052] Microfluidic channels were fabricated on a PDMS substrate using soft photolithography. The three-layer nanofiber membrane of the gradient filtration channel was prepared by electrospinning: the first layer was a polyacrylonitrile (PAN) membrane, 25 μm thick with an average pore size of 9 μm; the second layer was a polyvinylidene fluoride (PVDF) membrane, 22 μm thick with an average pore size of 3 μm; and the third layer was a polyethersulfone (PES) membrane, 28 μm thick with an average pore size of 0.8 μm. Each membrane surface was treated with oxygen plasma and then grafted with PVA hydrophilic groups. The sample inlet, outlet, and inner walls of the connecting channels of the microfluidic chip were coated with a heparin-chitosan cross-linked composite coating using a layer-by-layer self-assembly technique: the chip channel was first immersed in a 1% chitosan acetate solution for 10 min, then washed and immersed in a 1% heparin sodium solution for 10 min, repeated for 5 cycles to form a composite coating approximately 80 nm thick. Finally, the coating was vacuum dried and lightly plasma etched to form a micro / nano-uneven structure. The surface of the nanofiber membrane within the gradient filtration channel was not covered with an anti-coating coating to avoid clogging the nanopores.

[0053] Performance testing:

[0054] 1. Filtration efficiency test: Blood samples were taken from the fingertips of healthy volunteers and processed using this chip and conventional centrifugation (3000 rpm, 10 min). The serum yield and red blood cell residue were compared. The results are shown in Table 1.

[0055] 2. Anticoagulation stability test: After the treated serum samples were placed at room temperature for different times, the prothrombin time (PT) and activated partial thromboplastin time (APTT) were measured to evaluate the durability of the anticoagulation effect.

[0056] Example 3: Environmental Adaptability Verification of the Dynamic Baseline Calibration Module

[0057] Based on Example 1, this embodiment verifies the performance of the dynamic baseline calibration module under different environmental conditions to simulate typical usage scenarios such as high altitude (Lhasa, 3650 meters above sea level), high humidity (Guangzhou, summer) and low temperature (Harbin, winter).

[0058] Test method:

[0059] The testing system was placed in a programmable climate chamber (brand: ESPEC, model: SH-242), and the following three sets of environmental conditions were set respectively:

[0060] High-altitude simulation group: temperature 25℃, humidity 30%RH, air pressure 650hPa;

[0061] High humidity simulation group: temperature 30℃, humidity 85%RH, air pressure 1013hPa;

[0062] Low temperature simulation group: temperature 10℃, humidity 40%RH, air pressure 1013hPa.

[0063] Under each condition, after the system is powered on and stabilized, the initial baseline voltage is recorded. Then, environmental parameters and baseline voltage are collected every 5 minutes for 2 hours. The baseline drift is compared with and without the dynamic baseline correction function, and it is verified whether the coefficient of variation (CV) of the system for detecting 0.1 ng / mL cTnI standard is ≤5% under the condition of ±10% change in environmental parameters.

[0064] Example 4: Optimization of Antibody and Incubation Conditions in Specific Detection

[0065] This embodiment explores the effects of different antibody ratios, incubation temperatures, and times on detection sensitivity and specificity.

[0066] Antibody ratio optimization:

[0067] With a fixed total amount of fluorescent microspheres, four groups were set up with cTnI:CK-MB:NT-proBNP antibody ratios of 1:1:1, 2:1:1, 1:2:1, and 1:1:2. The same concentration of myocardial marker mixed standard (cTnI 1 ng / mL, CK-MB 5 ng / mL, NT-proBNP 50 pg / mL) was used for detection, and the fluorescence signal intensity and signal-to-noise ratio of each marker were compared.

[0068] Incubation conditions optimization:

[0069] Based on an antibody ratio of 1:1:1, different incubation protocols were set up: A) 37℃ for 15 min; B) 37℃ for 10 min + 40℃ for 5 min; C) 37℃ for 10 min + 40℃ for 10 min; D) 37℃ for 10 min + 42℃ for 5 min. The detection recovery rate and intra-batch precision of the three biomarkers under each protocol were compared.

[0070] Example 5: Validation of Signal Processing and Multi-Point Acquisition Filtering Strategy

[0071] This embodiment verifies the effect of the two-stage lock-in amplification structure and multi-point acquisition and screening strategy in the signal processing unit on improving the stability of the detection results.

[0072] Signal amplification performance test:

[0073] A series of low-concentration cTnI standards (0.01, 0.05, 0.1 ng / mL) were prepared and detected using conventional single-stage amplification and the two-stage lock-in amplification structure of this invention, respectively. The signal-to-noise ratio (SNR) and detection limit (LOD) of the signals were compared.

[0074] Multi-point data collection and filtering strategy test:

[0075] For the same reaction complex, single-point acquisition (center point), three-point acquisition, and the 5-8 point acquisition strategy of this invention (6 points in this experiment) were adopted respectively. Each acquisition point was continuously acquired 3 times. The signal screening module used the three-standard-deviation (3σ) criterion to remove abnormal signal values: the mean μ and standard deviation σ of the three measurements at the same acquisition point were calculated. If any measurement value exceeded the range of [μ−3σ,μ+3σ], it was considered abnormal and removed. If all values ​​were valid, their mean was taken as the signal value of that point. The final fluorescence signal value was the median of the signal values ​​of all valid acquisition points. The same medium concentration sample (cTnI 1ng / mL) was repeatedly detected 10 times, and the coefficient of variation (CV) of the detection results under different strategies was compared.

[0076] Comparative Example 1: Traditional centrifugation combined with enzyme-linked immunosorbent assay (ELISA)

[0077] To highlight the advantages of this invention, a control group using traditional methods was set up. The same clinical samples were used, and routine venous blood collection and serum separation were performed. Subsequently, commercially available ELISA kits (cTnI: Abcam ab200015; CK-MB: Abcamab131048; NT-proBNP: R&D Systems DY9719) were used to strictly follow the instructions for testing. The differences between the two methods in terms of sample processing time, serum volume, detection sensitivity, precision, and concordance with clinical diagnosis were compared.

[0078] Comparison Example 2: Detection system without dynamic baseline calibration

[0079] Based on the system of Example 1, the dynamic baseline calibration function was turned off, and only the initial baseline at startup was used for background subtraction. The detection was carried out under the same three sets of environmental simulation conditions as in Example 3, and the differences between it and the system of the present invention in terms of baseline stability and repeatability of detection results were compared.

[0080] Control Example 3: Using non-specific antibodies or common fluorescent dyes

[0081] Based on Example 4, the specific antibody was replaced with non-immune IgG of the same species, or the time-resolved fluorescent microspheres were replaced with ordinary FITC fluorescent dye. Other conditions remained unchanged, and the same standard was tested to evaluate the impact of non-specific binding background signal and dye stability on the detection results.

[0082] Experimental data and analysis description

[0083] The following experimental data are a compilation of the above examples and control examples.

[0084] Table 1: Comparison of serum treatment effects between microfluidic chip and centrifugation methods (n=10)

[0085]

[0086] Analysis: The microfluidic chip gradient filtration channel used in this invention achieves precise sieving through three layers of modified nanofiber membranes with different pore sizes. It can obtain a high yield (>60%) and extremely low red blood cell residue (0.05×10⁻⁶) from a trace amount of finger-prick blood (50μL) within a very short time (5min). 6 The serum yield of 10 ...

[0087] Table 2: Comparison of baseline drift under different environmental conditions (voltage, V)

[0088]

[0089] Analysis: Fluctuations in environmental parameters (especially temperature and air pressure) can significantly affect the electronic baseline of the detection system, leading to background signal drift. As shown in Table 2, without dynamic baseline calibration, the baseline drift can reach more than 0.1V within 2 hours under different harsh environments, which seriously interferes with the accurate detection of low-concentration signals. However, this invention, through high-frequency (10Hz) environmental sensing and a fast graded correction strategy based on FPGA, can strictly control the baseline drift within 0.01V, ensuring the stability and reliability of the detection system under different usage scenarios.

[0090] Table 3: Recovery rate and precision of myocardial biomarkers under different incubation regimens (n=5)

[0091]

[0092] Analysis: Incubation conditions are crucial to the efficiency of antigen-antibody binding. Scheme A (single temperature) showed low recovery and poor precision (CV > 8%), indicating that the binding was not optimal. Schemes B and C employed a segmented temperature increase strategy, first promoting initial binding at 37℃ and then increasing to 40℃ to accelerate the reaction and improve binding specificity. Both achieved excellent recovery (> 94%) and precision (CV < 5.5%), with Scheme C being slightly better. Scheme D, with its excessively high temperature (42℃), may have caused partial antibody inactivation and decreased performance. Therefore, the segmented isothermal strategy of 37℃ for 10 min + 40℃ for 5-10 min adopted in this invention is the optimized scheme.

[0093] Table 4: Impact of Signal Processing and Acquisition Strategies on Detection Stability

[0094]

[0095] Analysis: For low-concentration target analytes (e.g., 0.1 ng / mL cTnI), the extraction of weak fluorescence signals is crucial. The two-stage lock-in amplification structure significantly improves the signal-to-noise ratio (SNR from 5.2 to 18.7) through low-noise amplification in the pre-stage and high-gain amplification in the post-stage. Meanwhile, the multi-point acquisition and screening strategy (6-point acquisition, 3 times per point, and benchmark value after removing outliers) effectively overcomes random errors such as local inhomogeneity of the reaction complex and light source fluctuations, greatly reducing the coefficient of variation (CV) from 12.5% ​​in single-point acquisition to 3.1%, which greatly improves the repeatability and reliability of the detection results.

[0096] Table 5: Comparison of clinical sample detection results between the method of this invention and the ELISA method (n=30)

[0097]

[0098] Analysis: The detection results of the system of this invention on 30 clinical samples were compared with the gold standard ELISA method. The data showed that the detection results of the two methods were highly correlated (r>0.99), and the median concentrations were very close, indicating that the method of this invention has excellent accuracy. In terms of clinical diagnostic concordance rate (based on the final comprehensive clinical diagnosis result), the concordance rate of the method of this invention for cTnI, CK-MB, and NT-proBNP was 96.7%, 93.3%, and 100%, respectively, which is comparable to the ELISA method. However, the sample size required by this invention is only 1 / 10 of that of the ELISA method, the detection time is shortened from several hours to 25 minutes, and rapid on-site detection of finger-prick blood is achieved, demonstrating a huge advantage in clinical convenience.

[0099] Table 6: Key Performance Indicators for Comparison with Control Examples

[0100]

[0101] Comprehensive analysis:

[0102] The core innovation of this invention lies in the deep integration of three major technical modules: "dynamic baseline calibration," "microfluidic gradient filtering," and "time-resolved fluorescence multi-point detection." This integration constructs a rapid, sensitive, stable, and environmentally adaptable integrated detection system suitable for fingertip blood samples. Specific implementation methods and a series of control experimental data fully reveal and verify the following key conclusions:

[0103] 1. Significant advantages of system integration: The method proposed in this invention successfully integrates sample pretreatment, environmental interference suppression, specific biometrics and high-precision signal analysis into a compact process. Example 1 shows the complete operation process, which can complete the entire process from blood collection to quantitative analysis in about 25 minutes, meeting the timeliness requirements of point-of-care testing (POCT). This overcomes the inherent shortcomings of conventional technical processes, such as control example 1 (traditional ELISA), which are cumbersome and time-consuming.

[0104] 2. The contributions of key technology modules are clearly defined:

[0105] Microfluidic gradient filtration (Example 2): Through the gradient pore size design and specific surface modification of the three-layer modified nanofiber membrane, efficient and rapid separation of red blood cells and impurities in a small amount of fingertip blood was achieved, and high-quality serum was obtained, laying the sample foundation for subsequent high-sensitivity detection (see Table 1).

[0106] Dynamic baseline calibration (Example 3): High-frequency environmental perception and FPGA-based rapid hierarchical correction strategy effectively overcome the interference of temperature, humidity and air pressure fluctuations on the electronic baseline of the detection system. As shown in Table 2, this module can strictly control the baseline drift at an extremely low level (≤0.01V), ensuring the signal stability of the system under different operating environments. This is the root cause of the significant error generated by the control example 2 (without dynamic calibration) system when the environment changes.

[0107] Specific detection and signal processing optimization (Examples 4 and 5): The segmented isothermal incubation strategy optimized the antigen-antibody reaction kinetics, improving the recovery rate and precision of detection (see Table 3). The two-stage lock-in amplification structure combined with the multi-point acquisition screening strategy significantly improved the signal-to-noise ratio (SNR) of weak fluorescence signals and greatly reduced the random error (coefficient of variation) of detection, thereby ensuring the accuracy and repeatability of low-concentration target detection (see Table 4). This is different from the high background noise and low specificity problems exhibited by Control Example 3 (using non-specific reagents).

[0108] 3. Overall performance has been clinically validated: As shown in Table 5, the detection results of the system of the present invention on clinical samples are highly consistent with the gold standard ELISA method (correlation coefficient r > 0.99), and the concordance rate with the final clinical diagnosis is comparable. This comprehensively proves that the system not only has excellent analytical performance, but also has direct clinical practical value. At the same time, its requirement for micro-samples (finger-prick blood) and rapid detection capability is an important supplement and breakthrough to the existing laboratory testing mode.

[0109] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting myocardial biomarkers in fingertip blood based on dynamic baseline, characterized in that, Includes the following steps: S1: Sample pretreatment: After collecting a fingertip blood sample using a fingertip lancet, the sample is injected into the sample inlet of a microfluidic chip with a built-in gradient filtration channel. The gradient filtration channel of the microfluidic chip is used to separate red blood cells and impurities in the sample, and the inner wall of the chip is pre-coated with an anticoagulant coating. The sample is driven to flow through the gradient filtration channel by a built-in micropump to achieve the separation of red blood cells and impurities. Combined with the anticoagulant coating, anticoagulant treatment is completed, and finally, the serum sample to be tested is collected from the sample outlet of the chip. S2: Dynamic baseline calibration, setting up a baseline acquisition unit, an environmental parameter sensing unit, and a baseline dynamic correction unit; the baseline acquisition unit acquires the dark current signal of the optical detection unit under conditions of no sample and no excitation light source, and converts it into the initial electrical signal baseline of the detection system under no-load conditions through the signal acquisition circuit; the sampling point of the baseline acquisition unit is located on the signal path between the output end of the photomultiplier tube and the front-end low-noise pre-amplification module; The environmental parameter sensing unit collects the temperature, humidity and air pressure parameters of the detection environment in real time, and the baseline dynamic correction unit corrects the initial electrical signal baseline in real time according to the changes of the above parameters to obtain a dynamic calibration baseline. S3: Specific detection: The pretreated serum sample to be tested is quantitatively injected into the reaction cell, and a preset amount of myocardial marker-specific antibody labeled with fluorescent probe is added simultaneously; the reaction cell has a built-in oscillating mixing structure, and after being mixed evenly by the oscillating mixing structure, it is incubated at a constant temperature of 37°C for a preset time to form a reaction complex; an optical detection module is set up to scan the reaction complex and collect the fluorescence signal it generates. S4: Signal processing and result output. A signal processing unit and a result output unit are set up. Based on the dynamic calibration baseline obtained in step S2, background interference from the fluorescence signal is subtracted. The correction accuracy of the dynamic calibration baseline ensures that, under environmental parameter variations of ±10%, the system's detection coefficient of variation (CV) for the 0.1 ng / mL cTnI standard is ≤5%. After noise reduction and signal enhancement by the signal processing unit, the signal is compared with a preset standard curve for myocardial markers to complete the quantitative analysis of myocardial markers. The detection results are output through the result output unit.

2. The method according to claim 1, characterized in that, In step S1, the gradient filtration channel of the microfluidic chip is a three-layer modified nanofiber membrane stacked structure, with hydrophilic modified groups grafted onto the surface of each nanofiber membrane. The pore size decreases gradually from the sample inlet to the sample outlet at 8-10 μm, 2-5 μm, and 0.5-1 μm, respectively, specifically intercepting red blood cells, large-sized impurities, and small molecule impurities. The anticoagulant coating is a heparin-chitosan cross-linked composite coating, with a micro-nano textured structure constructed on the coating surface to enhance anticoagulant stability. The sample inlet of the microfluidic chip... The inner walls of the end, sample outlet, and connecting pipe are all covered with the heparin-chitosan cross-linked composite anticoagulant coating. The gradient filtration channel is composed of three independent modified nanofiber membranes. Each membrane is activated by oxygen plasma and bonded to a pre-reserved groove on the PDMS substrate. The membrane surface is not covered with an anticoagulant coating to avoid clogging the nanopores. The sample processing flow rate is controlled at 5-10 μL / min through a micro-pump closed loop. The chip temperature is maintained at 25-30℃ during the processing, and the processing time is 3-5 min.

3. The method according to claim 1, characterized in that, In step S2, the environmental parameter sensing unit and the baseline dynamic correction unit adopt a real-time linkage feedback mechanism. The environmental parameter sensing unit synchronously collects temperature, humidity, and air pressure parameters at a sampling frequency of 10Hz and performs multi-parameter fusion preprocessing. When the change of any environmental parameter exceeds a preset threshold, the baseline dynamic correction unit activates a graded correction strategy, specifically: the parameter change amplitude is divided into three levels. For level one change, the correction response speed is set to 3-7ms / time, and the correction amplitude is 0.001-0.003V; for level two change, the correction response speed is set to 2-5ms / time, and the correction amplitude is 0.004-0.006V; for level three change, the correction response speed is set to 0.5-2ms / time, and the correction amplitude is 0.008-0.012V. The preset thresholds for temperature, humidity, and air pressure changes are ±0.5℃, ±5%RH, and ±5hPa, respectively, and the corrected dynamic calibration baseline must meet the requirement that the baseline drift is ≤0.01V.

4. The method according to claim 1, characterized in that, In step S3, the myocardial markers include cTnI, CK-MB, and NT-proBNP, and the corresponding specific antibodies are mixed in a 1:1:1 ratio, and each antibody has undergone affinity purification. The fluorescent probe is a time-resolved fluorescent microsphere with a surface-modified targeting group, and the targeting group precisely matches the specific binding site of the myocardial marker. The fluorescent microsphere has an excitation wavelength of 340 nm, an emission wavelength of 615 nm, and a fluorescence lifetime of ≥100 μs. The incubation process adopts a segmented isothermal strategy: first, it is incubated at 37℃ for 10 min, and then the temperature is increased to 40℃ for 5-10 min. The temperature fluctuation range of each stage of the segmented isothermal incubation is ≤±0.5℃, and the continuous oscillation of 50-80 r / min is maintained during the incubation process by the oscillating mixing structure built into the reaction cell.

5. The method according to claim 1, characterized in that, In step S4, the signal processing unit employs a two-stage lock-in amplification structure. The first stage is a low-noise pre-amplification module, and the second stage is a high-gain main amplification module. The amplification factor can be adjusted by 10 based on the fluorescence signal intensity. 3 -10 5 The standard curve is adaptively graded and adjusted; it is pre-constructed using a series of concentration gradients of myocardial biomarker standards, ranging from 0.01 to 100 ng / mL, with gradients set at 0.01, 0.1, 1, 10, 50, and 100 ng / mL, and the correlation coefficient R of the standard curve is [value missing]. 2 ≥0.995; During the quantitative analysis, a multi-point signal acquisition and screening structure is adopted. Specifically, 5-8 acquisition points are set for different regions of the same reaction complex. Each acquisition point is equipped with an independent signal acquisition branch and continuously acquires fluorescence signals 3 times. Abnormal signal values ​​are eliminated by the signal screening module using the three-times-standard-deviation (3σ) criterion: the mean μ and standard deviation σ of the three measurements at the same acquisition point are calculated. If any measurement value exceeds the range of [μ−3σ,μ+3σ], it is considered abnormal and eliminated. If all values ​​are valid, the mean is taken as the signal value of that point. The final fluorescence signal value is the median of the signal values ​​of all valid acquisition points.

6. A finger-prick blood myocardial biomarker detection system based on dynamic baseline, characterized in that, It includes a sample preprocessing module, a dynamic baseline calibration module, a specificity detection module, a signal processing module, and a result output module, with each module linked together sequentially along the detection process; The sample pretreatment module includes a finger-prick blood collection component, a microfluidic chip, and a micropump. The collection component includes a finger-prick blood collection needle and a sample collection tube. The outlet of the sample collection tube is sealed to the inlet of the microfluidic chip via a sealing connector. The micropump is connected in series between the sample collection tube and the microfluidic chip to drive the sample to flow through the chip. The microfluidic chip integrates a gradient filtration channel and an anticoagulant coating, and the outlet is provided with a serum collection chamber. The dynamic baseline calibration module includes a baseline acquisition unit, an environmental parameter sensing unit, and a baseline dynamic correction unit. The baseline acquisition unit is used to acquire the dark current signal of the optical detection unit under conditions of no sample and no excitation light source, and convert it into the initial electrical signal baseline of the detection system under no-load conditions through the signal acquisition circuit. The sampling point of the baseline acquisition unit is located on the signal path between the output terminal of the photomultiplier tube and the front-end low-noise pre-amplification module. The environmental parameter sensing unit is used to capture the temperature, humidity and air pressure parameters of the detection environment. The baseline dynamic correction unit is connected to the former two respectively, and corrects the initial electrical signal baseline according to the parameter changes to obtain the dynamic calibration baseline. The specific detection module includes a reaction cell, a temperature control unit, and an optical detection unit. The reaction cell is connected to the chip sample outlet via a pipe and has a built-in oscillating mixing structure. It is externally wrapped with a temperature control unit to maintain a temperature of 37°C. The optical detection unit includes an excitation light source, a filter assembly, and a signal acquisition element to acquire fluorescence signals. The signal processing module is connected to the dynamic baseline calibration module and the specific detection module respectively. It includes a baseline subtraction unit, a noise reduction unit and a signal amplification unit. It is used to perform baseline subtraction, noise reduction and signal enhancement processing on the fluorescence signal in sequence, and then compare it with the preset standard curve to complete the quantitative analysis of myocardial markers. The results output module connects to the signal processing module, displaying the analysis results in digital and curve form, and has data storage and export functions.

7. The system according to claim 6, characterized in that, The microfluidic chip is integrally formed using PDMS material via soft photolithography; the nanofiber membrane is prepared by electrospinning with a thickness of 20-30 μm; the anti-coating coating is formed by layer-by-layer self-assembly with a thickness of 50-100 nm, covering the sample inlet, sample outlet and inner wall of the connecting pipe outside the filter channel, but not covering the surface of the nanofiber membrane inside the gradient filter channel.

8. The system according to claim 6, characterized in that, The environmental parameter sensing unit includes a temperature sensor, a humidity sensor, and a barometric pressure sensor, all of which are distributed within the detection chamber of the detection system. The temperature sensor has a measurement accuracy of ±0.1℃, the humidity sensor ±2%RH, and the barometric pressure sensor ±1hPa. The sampling frequency of each sensor is 10Hz. The baseline dynamic correction unit uses an FPGA chip as its core processing unit.

9. The system according to claim 6, characterized in that, The optical detection unit includes a pulsed LED excitation source, a filter group, a photomultiplier tube, and a signal acquisition circuit; the excitation filter has a center wavelength of 340nm and a half-width of 10nm, and the emission filter has a center wavelength of 615nm and a half-width of 15nm; the photomultiplier tube response time is ≤1ns.

10. The system according to claim 6, characterized in that, The system also includes a power supply module, a wireless communication module, and a signal warning output module. The power supply module is a 2000mAh rechargeable lithium battery that supports stable 5V / 2A output and provides matching power to each module via a power management circuit. The wireless communication module uses the Bluetooth 5.0 protocol and is electrically connected to the signal processing module to achieve real-time transmission of detection data. The signal warning output module is connected to the wireless communication module, has pre-stored normal reference thresholds for different myocardial markers, and has built-in audible and visual warning components and a signal storage unit. When the detection value exceeds the corresponding preset threshold, it automatically issues a warning through sound and pop-up window, while also enabling historical storage and export of detection data.

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