Nanometer photon structure chip, preparation method thereof, detection method and related equipment

Abstract

The invention provides a nano photon structure chip, a preparation method thereof, a detection method and related equipment, and relates to the technical field of medical detection equipment, and the preparation method of the nano photon structure chip comprises the following steps: vertically dropwise adding a mixed solution containing PS nanoparticles and SDS onto a silicon wafer substrate; covering the area of the micro-wall template above the area where the mixed solution is dropwise added on the silicon wafer substrate, so that the PS nanoparticles are self-assembled to form a regular chain structure under the guidance of the micro-wall template and the SDS solution; dividing the regular chain structure into a plurality of detection areas, and fixing a marker detection antibody in each detection area to obtain the nano photon structure chip containing a plurality of marker detection antibody detection areas. The nano photon structure chip provided by the invention enhances an optical signal, and when the nano photon structure chip is applied to detection of biomarkers, low-concentration biomarkers in a biological sample can be quickly identified.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of medical detection equipment, and particularly relates to a nano-photonic structure chip and a preparation method and a detection method thereof and related equipment. BACKGROUND

[0002] The content stated in this part is only for strengthening the understanding of the background of the present disclosure, and thus can include information which does not constitute the prior art known to those of ordinary skill in the art.

[0003] At present, the detection of cerebral apoplexy in the clinic mainly relies on imageology examination means such as head CT (Computed Tomography) and MRI (Magnetic Resonance Imaging). These technologies have certain advantages in lesion positioning and nature judgment, but have obvious limitations: high equipment cost, complex operation process, strict requirements for detection environment, and are difficult to popularize and apply in emergency vehicles, primary medical units, rescue, family monitoring and other scenes.

[0004] In order to meet the needs of on-site rapid detection of cerebral apoplexy markers, a cerebral apoplexy marker detection method is proposed in the related art. For the onset of cerebral apoplexy, some biomarkers reflecting the degree of neuron damage will appear in the body fluids such as serum, plasma and cerebrospinal fluid. However, in the early stage of cerebral apoplexy, the concentration of these biomarkers is often very low, and the identifiable signal released during detection is very weak and difficult to be detected. Therefore, how to provide a biomarker detection method capable of enhancing these identifiable signals is very important for the early detection of diseases such as cerebral apoplexy. SUMMARY

[0005] The present disclosure provides a nano-photonic structure chip and a preparation method and a detection method thereof and related equipment, which at least partially overcome the technical problems of the biomarker detection method provided in the related art, such as complex operation, low sensitivity and long time consumption.

[0006] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a nano-photonic structure chip is provided, comprising: a silicon wafer substrate; a polystyrene (PS) nanochain structure attached to the silicon wafer substrate, the PS nanochain structure being a regular chain structure formed by self-assembly of PS nanoparticles under the guidance of a micro-wall template and a sodium dodecyl sulfate (SDS) solution; and a marker detection antibody fixed on the PS nanochain structure.

[0008] In some embodiments, the PS nanochain structure comprises a plurality of detection zones, each of which is fixed with a marker detection antibody.

[0009] In some embodiments, the plurality of detection zones have isolation belts therebetween, so as to form independent marker detection channels for the respective detection zones.

[0010] In some embodiments, the marker detection antibody comprises at least one of the following stroke marker antibodies: S100B antibody, GFAP antibody, UCH-L1 antibody.

[0011] According to one aspect of the present disclosure, a preparation method of a nanophotonic structure chip is also provided, comprising: vertically dropping a mixed solution comprising PS nanoparticles and SDS onto a silicon wafer substrate; covering a micro-wall template above the area where the mixed solution is dropped on the silicon wafer substrate, so that the PS nanoparticles self-assemble to form a regular chain structure under the guidance of the micro-wall template and the SDS solution; dividing the regular chain structure into a plurality of detection zones, and fixing a marker detection antibody in each detection zone to obtain a nanophotonic structure chip comprising a plurality of marker detection antibody detection zones.

[0012] In some embodiments, the concentration of the solution comprising PS nanoparticles is 2 mg / mL; and the concentration of the SDS solution is 1 mg / mL.

[0013] In some embodiments, the PS nanoparticles are PS nanospheres, and the diameter of the PS nanospheres is 500 nm.

[0014] In some embodiments, after the regular chain structure is divided into a plurality of detection zones, and a marker detection antibody is fixed in each detection zone to obtain a nanophotonic structure chip comprising a plurality of marker detection antibody detection zones, the method further comprises: performing a blocking treatment on the nanophotonic structure chip using a bovine serum albumin (BSA) solution.

[0015] According to one aspect of the present disclosure, a detection method of a nanophotonic structure chip is also provided, wherein the nanophotonic structure chip is any of the above-described nanophotonic structure chips, and the method comprises: obtaining a marker detection sample; dropping the marker detection sample onto the nanophotonic structure chip; obtaining a surface image of the nanophotonic structure chip; inputting the surface image of the nanophotonic structure chip into a pre-trained artificial intelligence (AI) model, and outputting the concentrations of different markers in the marker detection sample.

[0016] According to one aspect of the present disclosure, a detection device is also provided, comprising: an image acquisition module configured to acquire an image of the nanophotonic structure chip after reacting with the marker detection sample; and an image analysis module configured to analyze the acquired image to determine the concentrations of different markers in the marker detection sample.

[0017] The nanophotonic structure chip, the preparation method, the detection method and the related device provided in the embodiments of the present disclosure can form a regular PS nanochain structure with consistent arrangement direction and uniform spacing by guiding the self-assembly of PS nanoparticles through the micro-wall template and the SDS solution. The high specific surface area of the PS nanochain structure can increase the fixed density of the detection antibody, and the light signal enhancement mechanism of the PS nanochain structure can significantly enhance the light signal intensity in the detection of biomarkers (such as stroke markers), quickly identify the low-concentration biomarkers in the biological sample, and solve the problems of complex operation, long time consumption and dependence on large equipment in the traditional biomarker detection method, thereby meeting the requirements of rapid, portable and high-sensitivity on-site detection. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor. In the drawings: Figure 1 A schematic diagram of a nanophotonic structure chip in an embodiment of the present disclosure is shown. Figure 2 A schematic diagram of a stroke marker detection reaction in an embodiment of the present disclosure is shown. Figure 3 A flowchart of a preparation method of a nanophotonic structure chip in an embodiment of the present disclosure is shown. Figure 4 A flowchart of an alternative preparation method of a nanophotonic structure chip in an embodiment of the present disclosure is shown. Figure 5 A process flowchart of a preparation method of a nanophotonic structure chip in an embodiment of the present disclosure is shown. Figure 6 A flowchart of a detection method of a nanophotonic structure chip in an embodiment of the present disclosure is shown. Figure 7 A schematic diagram of the whole process from signal acquisition to diagnosis output in an embodiment of the present disclosure is shown. Figure 8 A schematic diagram of the structure and analysis process of a deep learning model in an embodiment of the present disclosure is shown. Figure 9The detection results of different body fluid samples in the embodiment of the present disclosure are shown. Figure 10 A flow chart showing the process of detecting a stroke on an ambulance using a nano-photonic structure chip in the embodiment of the present disclosure is shown. Figure 11 A schematic diagram showing the comparison of nano-chains before and after the detection of a body fluid sample in the embodiment of the present disclosure is shown. Figure 12 A schematic diagram of a detection device in the embodiment of the present disclosure is shown. Figure 13 A schematic diagram showing the detection results of a nano-photonic structure chip in the embodiment of the present disclosure is shown. Figure 14 A schematic diagram showing the detection results of another nano-photonic structure chip in the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, further detailed descriptions of the embodiments of the present disclosure will be given below with reference to the drawings. Here, the illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure, but not as a limitation of the present disclosure.

[0020] In addition, the drawings are only schematic illustrations of the present disclosure, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. These functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0021] For ease of understanding, before introducing the embodiments of the present disclosure, first, several terms involved in the embodiments of the present disclosure are explained as follows: PS: Polystyrene, polystyrene.

[0022] SDS: Sodium Dodecyl Sulfate, Sodium Dodecyl Sulfate.

[0023] BSA: Bovine Serum Albumin, Bovine Serum Albumin.

[0024] EDC: A kind of carbodiimide cross-linking agent, the main role is to activate carboxyl (-COOH).

[0025] NHS: Used in combination with EDC, it combines with the activated carboxyl group to form a more stable ester intermediate, improves the efficiency and yield of the cross-linking reaction, and helps the antibody to be more stably covalently coupled to the gap or surface of the carrier such as nanosphere.

[0026] SeNPs: Selenium Nanoparticles, selenium nanoparticles.

[0027] The nano-photonic structure chip with light signal enhancement mechanism provided in the embodiments of the present disclosure forms a regular PS nanochain structure with consistent arrangement direction and uniform interval through the self-assembly of PS nanoparticles guided by a micro-wall template and SDS solution. The high specific surface area of the PS nanochain structure can increase the fixing density of the detection antibody. The light signal enhancement mechanism of the PS nanochain structure can significantly enhance the light signal intensity in the detection of biomarkers (such as stroke markers), quickly identify biomarkers with low concentration in a biological sample, and solve the problems of complex operation, long time consumption, and dependence on large equipment in the traditional biomarker detection method, thereby meeting the requirements of rapid, portable, and high-sensitivity on-site detection.

[0028] Exemplarily, as shown in Figure 1 The nano-photonic structure chip provided in the embodiments of the present disclosure includes a silicon wafer substrate 10, a PS nanochain structure 20 attached to the silicon wafer substrate, and a marker detection antibody (as shown in Figure 1 30) fixed on the PS nanochain structure. The PS nanochain structure is a regular chain structure formed by the self-assembly of PS nanoparticles guided by a micro-wall template and a sodium dodecyl sulfate (SDS) solution.

[0029] In an optional embodiment, the PS nanoparticles are PS nanospheres, and the PS nanochain structure is formed by the self-assembly of PS nanospheres with a diameter of 500 nm guided by a micro-wall template and an SDS solution. The antibody fixing site can be covalently coupled to the gap between the nanospheres through EDC / NHS.

[0030] It should be noted that in the embodiments of the present disclosure, the PS nanochain structure 20 formed by the self-assembly of PS nanospheres guided by a micro-wall template and an SDS solution is an equal-interval structure with ordered nanochains, which can solve the problem of unstable signal caused by the disorder of traditional photonic crystals.

[0031] In some embodiments, the PS nanochain structure can include a plurality of detection zones, and each detection zone is fixed with a marker detection antibody. Through this embodiment, the detection of multiple markers can be realized.

[0032] Further, in some embodiments, an isolation belt can be arranged between the plurality of detection zones on the PS nanochain structure, so as to form independent marker detection channels for each detection zone. Through this embodiment, the nano-photonic structure chip can realize the rapid detection of multiple-channel markers.

[0033] It should be noted that the nano-photonic structure chip provided in the embodiments of the present disclosure can be designed in size and shape (which can be but is not limited to a rectangle or a square) according to actual needs. In an optional embodiment, when the nano-photonic structure chip contains four independent detection channels (such as the first detection channel, the second detection channel, the third detection channel and the fourth detection channel shown in Figure 1 FIG. 1), a square structure can be adopted, and the size thereof can be x = 2.5 cm and y = 2.5 cm.

[0034] Optionally, the nano-photonic structure chip provided in the embodiments can be used for detection of any one or more biomarkers. When the nano-photonic structure chip provided in the embodiments is used for detection of stroke markers, one or more of the following marker detection antibodies can be fixed on the PS nanochain structure: S100B antibody, GFAP antibody, UCH-L1 antibody.

[0035] Stroke is one of the main causes of death and disability worldwide, and has the characteristics of acute onset, rapid progression and narrow treatment window. In particular, ischemic stroke (IS) accounts for about 80% of all stroke cases, and its effective intervention is highly dependent on early identification and classification diagnosis. Studies have shown that the best intervention effect is within 3 hours after stroke onset, and completing typing and processing within 1 hour after stroke onset (i.e., the "golden hour") is the key to improving the cure rate.

[0036] Currently, stroke diagnosis in the clinic mainly relies on head CT and MRI imaging examination, but such devices have problems such as high cost, complex operation, high environmental requirements, etc., which limit their application in ambulances, primary medical units, rescue, home monitoring and other scenarios. At the same time, during the acute phase of stroke, patients often show confusion, speech disorders, etc., and are difficult to cooperate with complex operations, further increasing the difficulty of diagnosis.

[0037] Based on this, in recent years, researchers have gradually explored rapid stroke diagnosis methods based on biomarkers in serum or body fluids. After the occurrence of stroke, the blood-brain barrier is damaged, and a large number of central nervous system components are released into the blood, forming a detectable "biological signal". Representative markers include: S100B protein: a sensitive indicator of astrocyte damage; GFAP (glial fibrillary acidic protein): used to identify hemorrhagic stroke; UCH-L1 (neuron-specific enolase): reflects neuronal damage; IL-6, TNF-a, MMP-9, etc. inflammatory factors; D-dimer, homocysteine, etc. vascular injury and coagulation abnormality indicators.

[0038] Although the above markers have shown diagnostic value in some studies, the existing detection methods have problems such as low sensitivity, long response time, and inability to detect multiple channels in parallel, making it difficult to support reliable judgment in a short time in the clinic.

[0039] The current mainstream scheme for detecting stroke markers is as follows: 1) Insufficient detection sensitivity, difficult to identify early low-concentration biomarkers of stroke: Within 15-60 minutes after the onset of stroke, the concentration of related markers (such as S100B, GFAP, UCH-L1, etc.) in the blood is extremely low, often in the order of pg / mL or even lower; the sensitivity of conventional ELISA, chemiluminescence, colloidal gold, etc. detection means is mostly in the order of ng / mL, which is difficult to accurately identify early patients; false negatives are prone to occur, affecting rapid decision-making and classification judgment.

[0040] 2) Slow detection speed, complicated operation process, difficult to meet the decision-making needs within the "golden time window" of stroke: The detection period of most current immunological detection methods (such as ELISA) is generally more than 1-3 hours, including multiple washing, incubation, and color development steps; it is not suitable for rapid application in emergency sites, primary clinics, or mobile emergency vehicles; it requires high-skilled operators and has low automation.

[0041] 3) Unable to achieve multiple marker parallel detection, insufficient information dimension: Single marker detection is difficult to support multi-dimensional diagnostic needs such as stroke type classification (such as ischemia vs. hemorrhage) and prognosis evaluation; most platforms do not have multi-channel detection structure or signal differentiation capability, and cannot achieve simultaneous detection of S100B, GFAP, UCH-L1, etc.; leading to increased misdiagnosis and missed diagnosis rates.

[0042] 4) Complex platform, poor integration, difficult to achieve portable or bedside application: Surface-enhanced Raman, fluorescent quantum dots, microelectrode arrays, etc. have strong dependence on equipment and complex structure; many laboratory-level devices have engineering problems in signal reading, antibody fixation, liquid control, etc., and have not been commercialized or are difficult to mass-produce; it is difficult to deploy in military field, rural health stations, community clinics, disaster emergency, etc.

[0043] 5) Lack of standardized, batch-preparable nanostructure system, poor process consistency: Some nano-probe platforms based on self-assembly or microfluidic construction have complex structure and poor repeatability; poor antibody fixation stability, chip packaging difficulty limit clinical transformation and product application; large batch differences affect detection accuracy and industrial scalability.

[0044] In this context, the present disclosure proposes Figure 1The nano-photonic structure chip shown adopts a light signal enhancement mechanism, a programmable heterogeneous array, and a multi-antibody co-loading system to realize super-sensitive and rapid detection of multiple channels and multiple markers, and has wide application prospects in the fields of precision medicine, disaster emergency, and primary health care.

[0045] Figure 2 The stroke marker detection reaction principle diagram is shown as Figure 2 As shown, the process of combining the antigen (stroke marker) 201 with the chip surface antibody 202 is shown; Figure 2 The antigen shown in 201 is combined with the SeNPs labeled compound shown in 203 to obtain a SeNPs-antigen compound; the SeNPs-antigen compound is combined with a polystyrene PS-antibody compound, and finally the optical signal is enhanced, and the enhanced optical signal 205 is shown.

[0046] In the embodiments of the present disclosure, through the light signal enhancement mechanism of the nano-photonic structure chip and the antibody array fixation technology, combined with the deep learning image recognition algorithm and the terminal image acquisition system, the embodiments of the present disclosure can provide a multi-channel stroke marker rapid detection platform. The platform prepares a nano-photonic structure chip with a regular heterogeneous chain structure, combines multiple antibodies and parallel fixation, realizes super-sensitive detection of key biological markers (such as S100B, GFAP, UCH-L1, etc.) of stroke at an early low concentration stage, and acquires the chip surface image through the camera of a terminal device such as a mobile phone, analyzes the color characteristics by using an AI model to realize concentration recognition, and finally outputs the concentration recognition quantitative result and preliminary diagnosis suggestion.

[0047] Based on the same inventive concept, the embodiments of the present disclosure provide a preparation method of a nano-photonic structure chip, which can be but is not limited to any of the above nano-photonic structure chips, such as Figure 3 As shown, the preparation method includes the following steps: S302, vertically dropping a mixed solution containing PS nanoparticles and SDS onto a silicon wafer substrate; S304, covering the micro-wall template above the area where the mixed solution is dropped on the silicon wafer substrate, so that the PS nanoparticles are self-assembled to form a regular chain structure under the guidance of the micro-wall template and the SDS solution; S306, dividing the regular chain structure into multiple detection zones, fixing one marker detection antibody in each detection zone, and obtaining a nano-photonic structure chip containing multiple marker detection antibody detection zones.

[0048] In some embodiments, the concentration of the above-mentioned solution containing PS nanoparticles is 2 mg / mL; the concentration of the SDS solution is 1 mg / mL.

[0049] In some embodiments, the PS nanoparticles are PS nanospheres, and the diameter of the PS nanospheres is 500 nm.

[0050] In some embodiments, as shown in Figure 4 After dividing the rule chain structure into multiple detection zones, fixing one marker detection antibody in each detection zone, and obtaining the nanophotonic structure chip containing multiple marker detection antibody detection zones, the preparation method of the nanophotonic structure chip provided in the embodiments of the present disclosure can further include the following steps: S308, using a bovine serum albumin (BSA) solution to perform blocking treatment on the nanophotonic structure chip.

[0051] Figure 5 As shown in the process flow chart for preparing the nanophotonic structure chip, as shown in Figure 5 As shown in the process flow chart for preparing the nanophotonic structure chip, as shown in S502, silicon wafer pretreatment: ultrasonic cleaning followed by low-temperature plasma treatment; S504, self-assembly: dropwise addition of a PS nanosphere solution containing 0.1% SDS, chain formation under the joint guidance of capillary force and nanowall template; S506, thermal curing: heating at 115°C for 35 min to stabilize the structure; S508, surface activation: treatment with an EDC / NHS mixed solution for 30 min; S510, antibody immobilization: dropwise addition of a 20 μg / mL antibody solution and incubation for 1 h to form a 207 polystyrene (PS)-antibody complex; S512, blocking: 1% BSA solution to block non-specific sites.

[0052] Based on the same inventive concept, the embodiments of the present disclosure provide a detection method for a nanophotonic structure chip, which can be but is not limited to any of the above-mentioned nanophotonic structure chips, as shown in Figure 6 The detection method includes the following steps: S602, obtaining a marker detection sample; S604, dropwise addition of the marker detection sample to the nanophotonic structure chip; S606, obtaining a surface image of the nanophotonic structure chip; S608, inputting the surface image of the nanophotonic structure chip into a pre-trained artificial intelligence (AI) model, and outputting the concentrations of different markers in the marker detection sample.

[0053] Figure 7 As shown in the full process from signal acquisition to diagnosis output, as shown in Figure 7 As shown in the full process from signal acquisition to diagnosis output, as shown in S702, hardware layer: the nanochip generates an optical signal, and the mobile phone camera collects the image; S704, pre-processing layer: color correction (eliminate ambient light interference), detection area segmentation (locate ROI of each channel); S706, AI analysis layer: CNN model extracts features, and regression calculates marker concentration; S708, output layer: display S100B / GFAP / NSE concentration values and classification suggestions (such as "AIS high risk").

[0054] In one embodiment, the AI model in the embodiments of the present disclosure can be implemented by using a deep learning model.

[0055] Figure 8 The structure of the deep learning model and the analysis process are shown in the schematic diagram as shown in the figure. Figure 8 The deep learning model includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The analysis process includes: S802, input layer: input the surface image after the nanophotonic chip reacts with the marker detection sample.

[0056] S804, convolutional layer: local feature extraction is performed on the surface image by sliding the convolutional kernel, which is highly matched with the signal characteristics of the nanophotonic chip.

[0057] The periodic structure of the PS nanochain appears as a microtexture in the image, and the convolutional kernel can identify the cooperative change of the light signals of adjacent nanoparticles (such as the spatial correlation of the resonance peak shift) and extract local patterns related to marker binding (such as the brightness change gradient of the hot spot area); specifically bound markers will form regular light signal changes in the antibody fixed area (consistent with the arrangement direction of the nanochain), and non-specific binding signals are randomly distributed. The convolutional layer can effectively enhance the specific signal features (such as extracting the brightness stripes arranged in a certain direction) and suppress the background noise by learning the edge and texture features.

[0058] S806, pooling layer: by downsampling the feature map output by the convolutional layer, the function of retaining key concentration-related features and reducing data dimensions is realized. The higher the marker concentration, the larger the area of the nanochain surface light signal enhancement, and the pooling operation can retain the area with the highest brightness and the most significant texture in the image (corresponding to the strong signal of the hot spot area) and filter secondary information (such as weak interference in the edge area). For example, when the S100B protein concentration in the detection sample is in the pg / mL level, the pooling layer can preferentially retain the features of a few strong signals, while for high concentration (ng / mL level), it retains more extensive signal distribution features, realizing adaptive processing for different concentration ranges.

[0059] S808, fully connected layer: nonlinear integration of the feature vector after pooling is performed to establish a mapping relationship with the marker concentration.

[0060] The full connection layer can integrate the local texture features (such as signal fluctuations caused by changes in nanochain spacing) extracted by the convolution layer and the global intensity features (such as average brightness and signal area ratio) reserved by the pooling layer to form a comprehensive judgment of the concentration. The relationship between the optical signal of the nanophotonic chip and the concentration is not simply linear (such as the signal platform may appear due to resonance saturation at high concentration), and the full connection layer learns this nonlinear law through an activation function (such as ReLU, Sigmoid) to improve the accuracy of concentration prediction.

[0061] S810, output layer: output the concentration of the marker in the marker detection sample.

[0062] The output of the full connection layer is converted into a specific concentration value, and its accuracy depends on the effective extraction of the nanophotonic chip signal features by the previous layers. For example, for low concentration (pg / mL level) samples, the model can achieve accurate prediction by capturing the subtle signal changes of the hot spot area (such as the brightness difference of a single nanochain unit); for high concentration (ng / mL level) samples, the concentration value can be output based on the global features of the signal distribution (such as the area of the signal saturation region), and since the chip's signal enhancement mechanism has amplified the weak signal, the model's sensitivity to low concentration is significantly higher than that of traditional image analysis methods.

[0063] It should be noted that the traditional method relies on a single optical signal parameter (such as intensity value), which is easily disturbed by ambient light; while the AI model in the embodiments of the present disclosure uses multi-dimensional features (texture, spatial distribution, intensity gradient) of the image, combined with the high signal-to-noise ratio characteristics of the chip, the anti-interference ability is greatly improved. In the embodiments of the present disclosure, the deep learning model is used to process the surface image of the nanophotonic chip in layers, fully exploiting the intrinsic correlation between the light signal enhancement features of the PS nanochain structure and the marker concentration, and achieving accurate mapping from image to concentration, providing technical support for rapid and high-sensitivity detection of stroke markers.

[0064] Figure 9 The detection results of different body fluid samples are shown, such as Figure 9 As shown in the figure, the signal intensity of S100B detected in the serum sample, the serum sample, the saliva sample and the urine sample is: gray value 0.3, gray value 0.25, gray value 0.18, and gray value 0.15. Among them, the background interference suppression rate is > 95% (verified by 100 samples).

[0065] The nanophotonic structure chip provided in the embodiments of the present disclosure can be applied to but not limited to Figure 10 The application scenarios are shown, such as Figure 10 As shown in the figure, including: S1001, the first-aid personnel uses a mobile phone to shoot the chip on an ambulance; S1002, the mobile phone outputs the detection result, including: quantitative values of three proteins; S1003, the mobile phone uploads the detection result to the cloud for analysis; S1004, output analysis result: ischemic stroke.

[0066] Figure 11 A nano-chain contrast schematic diagram before and after the detection of a body fluid sample is shown. From Figure 11 It can be seen that the nano-photonic structure chip can clearly detect the markers in the body fluid sample.

[0067] Based on the same inventive concept, the disclosure also provides a detection device, as shown in Figure 12 The detection device 120 includes an image acquisition module 121 and an image analysis module 122.

[0068] The image acquisition module 121 is configured to acquire an image of the nano-photonic structure chip after reacting with the marker detection sample; and the image analysis module 122 is configured to analyze the acquired image to determine the concentrations of different markers in the marker detection sample.

[0069] In order to verify the effect of the nano-photonic structure chip in the embodiments of the disclosure, three specific experimental examples are listed below.

[0070] Experiment 1: Chip verification experiment for S100B detection in a mouse brain injury model.

[0071] 1) Purpose of the experiment: to verify the sensitivity, stability and rapid detection capability of the nano-photonic structure chip in detecting the brain stroke-related biomarker S100B protein.

[0072] 2) Experimental materials and equipment: Animal model: SPF C57BL / 6J male mice, 8 weeks old, weighing 22±2g; divided into normal control group and mild, moderate and severe brain injury model groups (using a free fall device to cause injury), n=3 in each group.

[0073] Chip preparation raw materials: modified silicon wafer, polystyrene nanospheres (500nm), SDS (sodium dodecyl sulfate); EDC / NHS, S100B monoclonal antibody, BSA (bovine serum albumin); selenium nanoparticles (SeNPs) coupled with the protein to be detected for amplifying the optical signal.

[0074] Equipment: 5μm micro-wall spacing silicon template; constant temperature oven, plasma cleaning instrument, microscope, spectrum reading instrument; smart phone, mobile phone APP.

[0075] 3) Chip preparation method: Surface patterning: Plasma treatment of silicon wafer substrate.

[0076] Nanochain structure formation: 3.5 μL of mixture containing 500 nm PS nanoparticles (2 mg / mL) and 1 mg / mL SDS was vertically dropped onto the silicon wafer substrate, and a micro-wall template was covered above the dropped mixture, and incubated at 56°C for 1 hour to form a regular chain structure.

[0077] Structure stabilization: The chip was heated at 115°C for 30 minutes to solidify the structure.

[0078] Antibody immobilization: After activation using EDC / NHS, 10 μg / mL anti-S100B antibody was dropped and incubated overnight at 4°C.

[0079] Non-specific site blocking: Blocking was performed using 10 mg / mL BSA at room temperature for 30 minutes.

[0080] 4) Detection experiment procedure: Post-injury blood sampling: The mice were subjected to eye ball blood sampling at 15 min, 30 min, 1 h, 3 h, and 6 h after injury, and the serum was separated for use.

[0081] Detection reaction: After incubation of the serum with SeNPs-labeled anti-S100B antibody for 5 minutes, the mixture was dropped into the detection area of the chip.

[0082] Image acquisition and analysis: The image of the chip surface was vertically taken using a mobile phone camera; APP automatically completes image uploading and analysis; the CNN model outputs the S100B concentration value, which is compared with the standard curve.

[0083] 5) Result explanation: The chip showed a good dose-response relationship with the change in S100B concentration, and the color change was significant; The detection sensitivity can reach 1 pg / ml, and the linear interval is 1 pg / m~100 ng / ml; The S100B level of the experimental group was significantly higher than that of the control group, and the concentration was positively correlated with the degree of injury; Each image was analyzed on the mobile phone within 3 seconds, and the output result had a correlation R²=0.93 with ELISA. The chip structure is stable, and the batch repeatability CV value is less than 5%.

[0084] 6) Conclusion: The S100B detection chip based on nanophotonic structure described in this experiment showed good sensitivity, rapid response ability, and image quantitative analysis function in the mouse brain injury model, and had scalability and clinical transformation potential, verifying the feasibility and effectiveness of the technical route of the invention.

[0085] Experiment two: multi-marker chip detection experiment of serum samples of clinical stroke patients.

[0086] 1) Purpose of the experiment: to evaluate the sensitivity, throughput, specificity and deep learning quantitative recognition ability of the nano-photonic chip platform described in the present application in detecting S100B, GFAP, UCH-L1 and other stroke-related biomarkers in real clinical samples, and to verify its feasibility and accuracy in clinical application.

[0087] 2) Information of clinical samples: Sample source: from hospitalized patients in the Department of Neurology of the hospital, approved by the relevant departments, and agreed by the patients themselves; Grouping (n=5 for each group): normal healthy volunteers; acute ischemic stroke (AIS) patients; acute hemorrhagic stroke (ICH) patients; TIA / mild stroke patients; sampling time: 3 hours, 6 hours, 12 hours after onset; Sample type: serum, stored at -80°C after low-temperature centrifugation.

[0088] 3) Chip preparation and antibody immobilization: The chip substrate and chain structure preparation process are the same as experiment one; The chip surface is divided into three functional channels, and anti-S100B, anti-GFAP and anti-UCH-L1 antibodies (concentration of 10 μg / mL) are immobilized respectively. The total area of the chip is about 2.5 cm x 2.5 cm; The activation, antibody grafting and BSA blocking processes are consistent.

[0089] 4) Detection process: ①Mix the serum sample with the working solution containing SeNPs, and incubate at room temperature for 5 minutes; ②Add 500 μL of the mixture to the chip detection area; ③Wait for 10 minutes for reaction to complete; ④Use the phone camera to capture the chip image and upload it to the relevant detection APP on the phone; ⑤The APP automatically performs: image standardization preprocessing (brightness / background correction); region segmentation identification (channels 1 / 2 / 3); CNN model concentration value prediction output; typing suggestion and preliminary interpretation (such as AIS type tendency, high risk index, etc.).

[0090] 5) Detection results and analysis: ①The serum S100B, GFAP and UCH-L1 concentrations of all stroke group patients were significantly higher than those of the healthy group; ②GFAP in hemorrhagic stroke is significantly higher (about 7 times of ischemic stroke) and appears earliest (<6 hours) with high specificity; S100B has higher and earlier peak value (24-48 hours) in hemorrhagic stroke; UCH-L1 is more sensitive to ischemic injury with later peak time (72 hours) consistent with known clinical regularity; ③Compared with hospital standard ELISA detection data, the Pearson correlation coefficient between the predicted value of the mobile phone APP and the true value is greater than 0.91; ④The overall detection time is not more than 20 minutes, and the mobile phone image processing and prediction time is about 2 seconds; ⑤The nano-photonic structure chip in the embodiment has good repeatability of three-channel detection, and the intra-batch coefficient of variation (CV) is less than 8%.

[0091] 6) Summary and value: The experiment shows that the chip has good sensitivity, specificity, multi-channel throughput capacity and mobile terminal quantitative function, and can be used for rapid stroke type recognition, bedside detection and auxiliary decision support in real clinical environment. Combined with deep learning algorithm and mobile terminal integrated system, the platform can be widely used in pre-hospital emergency, primary screening, remote diagnosis and rehabilitation monitoring after stroke, etc., and has significant transformation potential and social benefits.

[0092] Experiment three: S100B detection of multiple body fluid source samples and chip adaptability verification.

[0093] 1) Purpose of the experiment: To verify the applicability, detection performance and signal stability of the nano-photonic structure-based cerebral apoplexy marker detection chip in the present application in non-serum samples such as cerebrospinal fluid, saliva and urine, and to explore its feasibility in non-invasive or minimally invasive apoplexy auxiliary diagnosis.

[0094] 2) Sample source and processing: Sample type: Cerebrospinal fluid (CSF): from suspected cerebral apoplexy inpatients undergoing lumbar puncture (a total of 12 cases); saliva: voluntarily provided by stroke patients and healthy volunteers; urine: morning midstream urine, no medication before sampling; Grouping: stroke group (n=6): patients with confirmed acute ischemic stroke; control group (n=6): healthy volunteers; all samples were centrifuged at 5000 rpm for 5 min, the supernatant was taken, 0.22 μm filter membrane was used to remove impurities, and it was stored at -80℃.

[0095] 3) Chip configuration and detection process: ①The chip structure and S100B antibody channel construction method are the same as in experiment one; ②Each sample is diluted 1:2 before adding (optimized for body fluid osmotic pressure and background difference); ③ 500 μL treated sample mixed with SeNPs for 5 minutes; ④ Added dropwise to the chip detection area, incubated at room temperature for 10 minutes; ⑤ Take a picture, upload to APP, use the preset model for image analysis and return the concentration value.

[0096] 4) Results and analysis: The concentration of S100B in cerebrospinal fluid in the stroke group was significantly increased, and the chip color changed significantly. The detection value of the mobile phone terminal was correlated with the detection value of the hospital ELISA method, R²=0.89; The content of S100B in saliva and urine was low, but the chip could still stably identify levels greater than 50 pg / mL, indicating that the detection system provided by the embodiment of the disclosure also has certain detection capability for non-blood samples; The image of different body fluid samples can be automatically background adjusted and compensated by the mobile phone APP; The detection results show that the chip has good background noise control ability and sensitivity retention in complex body fluid environment.

[0097] 5) Innovation value and clinical expansion significance: This experiment first systematically verifies the application potential of nano-photonic structure chip in the detection of body fluids other than blood; For people who cannot quickly take blood or need non-invasive monitoring (such as the elderly population, primary screening), the chip platform provides a feasible detection alternative; The image preprocessing function optimized by AI algorithm can automatically adapt to the color and background changes of different body fluids, improving the robustness and intelligence level of the detection system; Support future early screening of brain injury, stroke recurrence risk assessment and other extended applications based on non-invasive samples.

[0098] 6) Summary: This experiment further verifies the cross-body fluid adaptability of the nano-photonic structure chip, which still has good sensitivity, stability and image recognition ability in non-serum samples, providing a key technical foundation for future truly non-invasive rapid stroke biomarker detection.

[0099] Compared with the multi-channel electrochemical detection technology based on microfluidic chip or surface enhanced Raman spectroscopy (SERS) detection technology, the present application has obvious advantages in detection sensitivity, detection speed, throughput capacity, platform adaptability, integration level and intelligence level, etc., as follows: 1) Higher detection sensitivity: The detection sensitivity of existing microfluidic / electrochemical sensors for stroke biomarkers is generally 100-500 pg / mL, and the response ability to very early low concentration signals is weak; The present application uses nano-photonic structure enhancement mechanism, through the light interference / scattering enhancement effect of equidistant PS nanochain, the detection sensitivity can reach 1 pg / mL, and it has good response to the release of biomarkers within 15-30 minutes of stroke onset.

[0100] 2) Faster response, adapting to the "golden 1 hour" of stroke emergency needs: The operation process of the microfluidic system still relies on pump control, washing, signal amplification and other steps, and the detection period often takes 30-90 minutes; the chip of the application does not need external equipment, and the whole process operation control is completed within 15 minutes, which adapts to the rapid detection needs of stroke emergency / bedside / flow diagnosis and treatment and other scenes.

[0101] 3) High detection flux, accurate judgment of multiple indicators in parallel: Existing SERS or electrode type sensors are mostly single indicator detection, lacking effective partition control and result reading mechanism; the application realizes multi-channel synchronous detection of S100B, GFAP, UCH-L1 and other indicators through functional area division and multi-antibody co-immobilization, supporting stroke type screening (such as ischemia or hemorrhage) and dynamic monitoring.

[0102] 4) More integrated system, more convenient operation: Existing technologies require complex detection platforms or large instruments (such as spectrometers, electrochemical workstations), which are highly dependent on operators; the application adopts an integrated design of smartphone camera + image recognition AI model + APP interface, realizing the minimalist interaction process of "taking a picture to get results", which can be completed independently by non-professionals.

[0103] 5) More suitable for a wide range of scenes, can be used for non-blood sample detection and remote medical treatment: Most existing detection schemes are only suitable for serum samples and cannot cope with complex clinical environments; the system can stably detect in cerebrospinal fluid, saliva, urine and other body fluids; it supports pre-hospital emergency, home monitoring, disaster relief, remote medical treatment and other multi-scene deployment, and has truly mobile and scene-based application capabilities.

[0104] 6) Easier batch production and engineering transformation: Existing complex SERS substrates or microfluidic chips have difficulties in consistency and cost control; The embodiments of the disclosure adopt standardized template self-assembly + thermal curing technology, which is simple in process, low in cost and good in repeatability, and has advantages in large-scale industrial transformation. The summary comparison table is shown in Table 1.

[0105] Table 1

[0106] The embodiments of the disclosure have been verified by animal model experiments, clinical sample detection, image recognition model training and actual measurement verification, and the results show that the technical scheme proposed by the disclosure is excellent in structure realization, detection performance, AI analysis accuracy and actual application feasibility, and has clear practical value and engineering transformation prospect. Specifically, it includes the following aspects: 1) Animal experiment verification (such as experiment one): In a mouse brain injury model, S100B protein was detected by a chip, and the results showed that the minimum detectable concentration of the chip was 1 pg / mL, and the detection results were highly correlated with ELISA (R² = 0.93); the overall detection time was not more than 15 minutes, the chip structure was stable, and the repeatability was good (CV < 5%); the stability of the nano-chain structure to enhance the signal and the antibody fixation was verified.

[0107] 2) Clinical sample detection verification (such as experiment two): In the serum samples of clinical stroke patients (ischemic / hemorrhagic) and healthy controls, S100B, GFAP, and UCH-L1 were detected, and the chip detection values were in good agreement with the standard ELISA results (Pearson r > 0.91); the chip supports simultaneous detection of three markers, and the results can be output by a mobile phone APP with one key; successful typing suggests AIS / ICH preliminary screening tendency, and the practicality and typing value of the platform in real clinical environment are verified.

[0108] 3) Multiple body fluid adaptability test (experiment three): The chip can stably detect S100B in cerebrospinal fluid, and also has good background noise control and low concentration recognition ability in saliva and urine; indicating that the present application has cross-sample platform adaptability and supports non-invasive or minimally invasive detection expansion.

[0109] 4) AI image recognition model training and actual measurement effect: Based on more than 1000 chip images with known concentrations, a convolutional neural network (CNN) model was trained, and the APP end test had high accuracy and fast response (image processing and concentration prediction took about 2 seconds); the model can automatically adapt to light and background differences on different mobile devices, and the result fluctuation range is < 10%; the user measurement interface is friendly and easy to operate, and truly realizes "instant reading and instant use".

[0110] The embodiments of the present disclosure not only complete controllable, stable, and standardized verification paths in structure and process, but also achieve significant results in biomarker recognition sensitivity, image reading intelligence, and operation portability. The related experimental results provide a solid technical and data foundation for the popularization and application of the present application in bedside detection, rapid stroke recognition, remote intelligent medical support system, and the like.

[0111] Figure 13 The results of the experiment of detecting the chip provided in the embodiments of the present disclosure using standard proteins of different concentrations (diluted from high to low) are shown. After evaluating the accuracy of the detection values and the actual concentrations, the results showed that the R² value was as high as 0.9995, indicating that the chip provided in the embodiments of the present disclosure was very accurate in quantitative detection.

[0112] At the same time, a human sample was selected for parallel verification, such as Figure 14As shown, the abscissa (X-axis) is the detection result using the traditional ELISA method (i.e., enzyme-linked immunosorbent assay method), and the ordinate (Y-axis) is the detection result using the chip provided in the embodiments of the present disclosure. The scatter plot drawn by comparing the two groups of data shows that the R² value reaches 0.9875. The closer the R² value is to 1, the higher the correlation and consistency of the results of the two methods. This indicates that the chip provided in the embodiments of the present disclosure is highly consistent with the traditional gold standard method in terms of detection results.

[0113] In summary, the nano-photonic structure chip provided in the embodiments of the present disclosure and the preparation method and detection method thereof and the related device can realize super-sensitive, rapid, multi-channel parallel detection of various stroke-related proteins by introducing a nano-scale heterogeneous chain structure to achieve optical signal enhancement, combining antibody recognition and programmable patterning array, and can achieve but are not limited to the following technical effects: 1) Self-assembly of heterogeneous nano-chain structure and photonic enhancement mechanism: the embodiments of the present disclosure innovatively use polystyrene (PS) nanospheres and SDS solution to self-assemble into an equidistant chain structure under a micro-wall template; optical interference and scattering enhancement are achieved, the biological binding signal is amplified, and the detection sensitivity is significantly improved, with a minimum detectable concentration of 1 pg / mL; the structure is heat-cured and fixed, and has good reproducibility and batch preparation capability.

[0114] 2) Multi-channel antibody fixed array for realizing parallel detection of multiple markers: the embodiments of the present disclosure regionalize the surface structure of the chip, and fix an antibody of a stroke-related protein (such as S100B, GFAP, UCH-L1) in each detection area; single sample loading can complete multi-index detection, typing and severity judgment.

[0115] 3) The chip can be directly matched with a smartphone camera to read images, and the results can be visualized and output: the embodiments of the present disclosure design a detection platform that is connected with a terminal (such as a mobile phone APP), and no professional detection instrument is needed; the detection platform is suitable for bed-side detection needs in various complex environments (such as ambulances and primary clinics).

[0116] 4) Image quantitative recognition model (CNN) based on deep learning: the chip image features (color change, reflection distribution, etc.) are input into a neural network, and the predicted value of the target protein concentration is output; the model has cross-device adaptability and supports continuous learning optimization.

[0117] 5) Compatible with various body fluids (serum, cerebrospinal fluid, saliva, urine) to realize detection of cerebral apoplexy.

[0118] It should be noted that the above modules and the examples and application scenarios realized by the corresponding steps are the same, but are not limited to the content disclosed in the above method embodiments. It should be noted that the above modules as part of the device can be executed in a computer system such as a group of computer executable instructions.

[0119] Those skilled in the art will appreciate that embodiments of the disclosure can be supplied as a method, a system, or a computer program product. Accordingly, the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the disclosure can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer-readable program code.

[0120] The disclosure is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the function specified by the flow or flows and / or block or blocks.

[0121] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the function specified by the flow or flows and / or block or blocks.

[0122] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the function specified by the flow or flows and / or block or blocks.

[0123] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the disclosure. It should be understood that the above-described specific embodiments are merely specific embodiments of the disclosure and are not used to limit the protection scope of the disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the disclosure shall be included in the protection scope of the disclosure.

Claims

1. A nanophotonic structure chip, characterized in that: include: Silicon wafer substrate; A polystyrene (PS) nanochain structure attached to the silicon wafer substrate, wherein the PS nanochain structure is a regular chain structure formed by self-assembly of PS nanoparticles under the guidance of a microwall template and sodium dodecyl sulfate (SDS) solution; The marker detection antibody is fixed on the PS nanochain structure.

2. The nanophotonic structure chip according to claim 1, characterized in that: The PS nanochain structure includes multiple detection areas, each of which is fixed with a marker detection antibody.

3. The nanophotonic structure chip according to claim 2, characterized in that: There are isolation zones between the multiple detection areas, so that each detection area forms an independent marker detection channel.

4. The nanophotonic structure chip according to claim 1, wherein: The marker detection antibodies include at least one of the following stroke marker antibodies: S100B antibody, GFAP antibody, and UCH-L1 antibody.

5. A method for preparing a nanophotonic structure chip, characterized in that: include: A mixed solution containing PS nanoparticles and SDS was vertically dropped onto a silicon wafer substrate; A microwall template is covered above the area on the silicon wafer substrate where the mixed solution is dropped, so that the PS nanoparticles self-assemble into a regular chain structure under the guidance of the microwall template and the SDS solution; The regular chain structure is divided into multiple detection areas, and a marker detection antibody is fixed in each detection area to obtain a nanophotonic structure chip containing multiple marker detection antibody detection areas.

6. The preparation method according to claim 5, wherein The concentration of the solution containing PS nanoparticles is 2 mg / mL; the concentration of the SDS solution is 1 mg / mL.

7. The preparation method according to claim 5, wherein The PS nanoparticles are PS nanospheres, and the diameter of the PS nanospheres is 500 nm.

8. The preparation method according to claim 5, wherein After dividing the regular chain structure into a plurality of detection areas and fixing a marker detection antibody in each detection area to obtain a nanophotonic structure chip comprising a plurality of marker detection antibody detection areas, the method further comprises: The nanophotonic structure chip is sealed using a bovine serum albumin (BSA) solution.

9. A method for detecting a nanophotonic structure chip, characterized in that: The nanophotonic structure chip is the nanophotonic structure chip according to any one of claims 1 to 4, and the method comprises: Obtain samples for marker testing; adding the marker detection sample dropwise onto the nanophotonic structure chip; Acquiring a surface image of the nanophotonic structure chip; The surface image of the nanophotonic structure chip is input into a pre-trained artificial intelligence (AI) model, and the concentrations of different markers in the marker detection sample are output.

10. A detection device, characterized in that: include: An image acquisition module, configured to acquire an image of the nanophotonic structure chip according to any one of claims 1 to 4 after the reaction with the marker detection sample; The image analysis module is used to analyze the collected image to determine the concentration of different markers in the marker detection sample.

Images