Nanochain structure chip, preparation method and detection method thereof and related equipment

Abstract

The present disclosure provides a nano-chain structure chip and a preparation method and a detection method thereof and related equipment, relates to the technical field of biological detection, and the detection method comprises the following steps: dropping a detection sample on the nano-chain structure chip, collecting multiple frames of images in a reaction process of the nano-chain structure chip and the detection sample; performing segmentation processing on each target image to obtain a genus detection area image, a species detection area image, a bacterial virulence detection area image and a blank control area image corresponding to each image; inputting the images into a pre-trained genus detection model, a species detection model and a bacterial virulence detection model respectively, outputting genus detection results, species detection results and bacterial virulence detection results of each image, and performing correlation analysis, and determining whether the detection sample contains microorganisms and the species and virulence of the microorganisms according to a correlation analysis result. The present disclosure can quickly realize full-chain detection of the genus, species and virulence.

Description

Technical Field

[0001] This disclosure relates to the field of biodetection technology, and in particular to a nanochain structure chip and its preparation method, detection method and related equipment. Background Technology

[0002] In scenarios such as food safety monitoring and public health emergency response, there is an urgent need for a rapid and accurate detection method for pathogenic microorganisms (such as pathogenic Vibrio). However, most current detection methods for pathogenic microorganisms focus on single-target detection, only able to detect a specific characteristic of the microorganism (such as a specific gene, protein, or antigen). For example, anti-LPS antibodies are used to identify a pathogen as Vibrio, and PCR (Polymerase Chain Reaction) is used to detect a specific virulence gene (such as TDH in Vibrio). When the source of the sample to be tested is unknown or there is a mixed microbial infection (for example, in food samples and wound secretions, there is often interference from non-target bacteria), it is difficult to accurately identify the species and virulence of the microorganism based on a single characteristic, which may lead to missed or false detections. Summary of the Invention

[0003] This disclosure provides a nanochain structure chip and its preparation method, detection method and related equipment, which at least to some extent overcomes the technical problem that the microbial detection methods provided in related technologies can only achieve the detection of a single target, and may have missed detection or false detection.

[0004] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0005] According to one aspect of this disclosure, a detection method for a nanochain structure chip is provided, comprising: dropping a detection sample onto the nanochain structure chip; acquiring multiple frames of images during the reaction process between the nanochain structure chip and the detection sample; wherein the nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area; the genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes with corresponding detection functions; segmenting each frame of the target image to obtain the genus detection area image, species detection area image, virulence detection area image, and blank control area image corresponding to each frame of the image; and segmenting the genus detection area image, species detection area image, virulence detection area image, and blank control area image corresponding to each frame of the image; and segmenting the genus detection area image, species detection area image, virulence detection area image, and blank control area image corresponding to each frame of the image. The detection area image and the blank control area image are input into a pre-trained bacterial genus detection model, and the bacterial genus detection result for each frame is output. The bacterial species detection area image and the blank control area image corresponding to each frame are input into a pre-trained bacterial species detection model, and the bacterial species detection result for each frame is output. The bacterial virulence detection area image and the blank control area image corresponding to each frame are input into a pre-trained bacterial virulence detection model, and the bacterial virulence detection result for each frame is output. Correlation analysis is performed on the bacterial genus detection results, bacterial species detection results, and bacterial virulence detection results of multiple frames to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms.

[0006] In some embodiments, each image frame corresponds to an acquisition timestamp; correlation analysis is performed on the genus detection results, species detection results, and virulence detection results of multiple images to determine whether the test sample contains microorganisms and the species and virulence of the microorganisms, including: performing time-dimensional color and / or brightness change analysis on the genus detection results of multiple images based on the acquisition timestamp corresponding to each image frame to obtain a first color and / or brightness change result; performing time-dimensional color and / or brightness change analysis on the species detection results of multiple images based on the acquisition timestamp corresponding to each image frame to obtain a second color and / or brightness change result; performing time-dimensional color and / or brightness change analysis on the virulence detection results of multiple images based on the acquisition timestamp corresponding to each image frame to obtain a third color and / or brightness change result; and performing correlation analysis on the first color and / or brightness change result, the second color and / or brightness change result, and the third color and / or brightness change result to determine whether the test sample contains microorganisms and the species and virulence of the microorganisms.

[0007] In some embodiments, each frame of the target image is segmented to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame. This includes: inputting each frame of the image into a pre-trained nanochain structure chip image segmentation model, and outputting the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame. The nanochain structure chip image segmentation model is a pre-trained model used to identify the genus detection area, species detection area, virulence detection area, and blank control area on the nanochain structure chip.

[0008] According to one aspect of this disclosure, a nanochain structure chip is also provided for application in any of the above detection methods. The nanochain structure chip includes: a chip substrate; and a nanoparticle chain structure fixed on the surface of the chip substrate. The nanoparticle chain structure is an equidistant array structure formed by the self-assembly of polystyrene (PS) nanoparticles under the guidance of a microwalled template and a sodium dodecyl sulfate (SDS) solution. The nanoparticle chain structure is divided into a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively fixed with antibodies or nucleic acid probes for corresponding detection functions.

[0009] In some embodiments, the nanochain structure chip is a chip for detecting pathogenic Vibrio, with anti-LPS antibody immobilized in the genus detection area; anti-OmpU antibody immobilized in the species detection area; and anti-TDH antibody immobilized in the virulence detection area.

[0010] In some embodiments, the nanochain structure chip is a chip for detecting Vibrio vulnificus, with anti-LPS antibody immobilized in the genus detection region; anti-OmpK antibody immobilized in the species detection region; and anti-TDH antibody immobilized in the virulence detection region.

[0011] In some embodiments, the nanochain structure chip is a chip for detecting Vibrio cholerae, with anti-LPS antibody immobilized in the genus detection region; anti-OmpT antibody immobilized in the species detection region; and ctxA complementary probe immobilized in the virulence detection region.

[0012] According to one aspect of this disclosure, a method for preparing a nanochain structure chip is also provided, for preparing the nanochain structure chip of any of the above claims, the method comprising: vertically dropping a mixture containing PS nanoparticles and SDS solution onto a chip substrate; depositing a microwall template on the chip substrate on which the mixture was dropped, so that the PS nanoparticles self-assemble into an equally spaced nanoparticle chain structure under the guidance of the microwall template and SDS solution; placing the chip with the dropped mixture in a constant temperature chamber, removing the microwall template from the surface of the chip substrate, so that the nanoparticle chain structure is fixed on the surface of the chip substrate, thereby obtaining a nanochain structure chip; treating the nanochain structure chip with an activator solution, and fixing antibodies or nucleic acid probes with corresponding detection functions in the genus detection area, species detection area, and virulence detection area of ​​the nanochain structure chip respectively; and blocking the nanochain structure chip with bovine serum albumin (BSA) solution.

[0013] According to one aspect of this disclosure, a detection device is also provided, comprising: an image acquisition module for dripping a detection sample onto a nanochain structure chip and acquiring multiple frames of images during the reaction process between the nanochain structure chip and the detection sample, wherein the nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area, wherein the genus detection area, species detection area, and virulence detection area are respectively fixed with antibodies or nucleic acid probes with corresponding detection functions; an image segmentation module for segmenting each frame of the target image to obtain the genus detection area image, species detection area image, virulence detection area image, and blank control area image corresponding to each frame of the image; and an AI detection module for: segmenting each frame of the image... The system inputs the corresponding genus detection area image and blank control area image into a pre-trained genus detection model, outputting the genus detection result for each frame; it inputs the corresponding species detection area image and blank control area image into a pre-trained species detection model, outputting the species detection result for each frame; it inputs the corresponding virulence detection area image and blank control area image into a pre-trained virulence detection model, outputting the virulence detection result for each frame; and it uses a correlation analysis module to perform correlation analysis on the genus detection results, species detection results, and virulence detection results of multiple frames to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms.

[0014] According to one aspect of this disclosure, a detection system is also provided, comprising: a nanochain structure chip of any of the above-mentioned types, for reacting with a detection sample; an image acquisition and analysis device for acquiring multiple frames of images during the reaction process between the nanochain structure chip and the detection sample; segmenting each frame of the target image to obtain a genus detection area image, a species detection area image, a virulence detection area image, and a blank control area image corresponding to each frame of the image; inputting the genus detection area image and the blank control area image corresponding to each frame of the image into a pre-trained genus detection model, and outputting the genus detection result of each frame of the image; and inputting the species detection area image and the blank control area image corresponding to each frame of the image into a pre-trained genus detection model. A pre-trained bacterial species detection model outputs the bacterial species detection results for each frame of image. The corresponding virulence detection area image and blank control area image for each frame are input into the pre-trained virulence detection model, which outputs the virulence detection results for each frame. Correlation analysis is performed on the genus detection results, species detection results, and virulence detection results of multiple frames to determine whether the sample contains microorganisms and the species and virulence of the microorganisms. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes with corresponding detection functions.

[0015] The nanochain structure chip, its preparation method, detection method, and related equipment provided in this disclosure are prepared by self-assembling nanoparticles into a nanoparticle chain structure under the guidance of a micro-wall template and a surfactant. This nanochain structure chip is used to detect various microorganisms. The nanochain structure chip includes a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes corresponding to the detection function. When detecting microorganisms, the test sample is dropped onto the nanochain structure chip, and multiple frames of images are acquired during the reaction process between the nanochain structure chip and the test sample. The nanochain structure chip includes a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes corresponding to the detection function. Each frame of the target image is segmented to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image. The genus detection area image and blank control area image are then compared. The system inputs data into a pre-trained bacterial genus detection model and outputs the bacterial genus detection result for each frame of the image. It also inputs the corresponding bacterial species detection area image and blank control area image into a pre-trained bacterial species detection model and outputs the bacterial species detection result for each frame. Furthermore, it inputs the corresponding bacterial virulence detection area image and blank control area image into a pre-trained bacterial virulence detection model and outputs the bacterial virulence detection result for each frame. Finally, it performs correlation analysis on the bacterial genus detection results, bacterial species detection results, and bacterial virulence detection results of multiple frames to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms.

[0016] The nanostructure chip provided in this embodiment can not only significantly enhance signal recognition capabilities and greatly improve the detection sensitivity of various microorganisms, but also, by dividing the nanostructure chip into a genus detection area, a species detection area, and a virulence detection area, and combining the genus detection model, species detection model, and virulence detection model pre-trained through machine learning to identify, detect, and correlate the acquired images, it can quickly achieve full-chain detection of genus, species, and virulence. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0018] Figure 1 This is a schematic diagram of a nanochain structure chip structure according to an embodiment of the present disclosure;

[0019] Figure 2 This is a schematic diagram illustrating the principle of a nanochain-antibody immobilization mechanism in an embodiment of this disclosure;

[0020] Figure 3 This is a schematic diagram illustrating the principle of multi-target Vibrio recognition in an embodiment of this disclosure;

[0021] Figure 4 This is a flowchart illustrating the method for detecting microorganisms using a nanochain structure chip in this embodiment of the present disclosure.

[0022] Figure 5 This is a schematic diagram illustrating the results of microbial detection using a nanochain structure chip in an embodiment of this disclosure;

[0023] Figure 6 This is a schematic diagram of a nanochain structure chip for detecting pathogenic Vibrio in an embodiment of this disclosure;

[0024] Figure 7 This is a schematic diagram of a nanochain structure chip for detecting Vibrio vulnificus in an embodiment of this disclosure;

[0025] Figure 8 This is a schematic diagram of a nanochain structure chip for detecting Vibrio cholerae in an embodiment of this disclosure;

[0026] Figure 9 This is a flowchart illustrating a method for fabricating a nanochain structure chip according to an embodiment of this disclosure;

[0027] Figure 10 This is a flowchart of a detection method for a nanochain structure chip according to an embodiment of the present disclosure;

[0028] Figure 11 This is a flowchart of an optional nanochain structure chip detection method according to an embodiment of this disclosure;

[0029] Figure 12 This is a flowchart illustrating an image recognition process based on artificial intelligence technology in an embodiment of this disclosure;

[0030] Figure 13 This is a schematic diagram of a detection device according to an embodiment of the present disclosure;

[0031] Figure 14 This is a schematic diagram of a detection system according to an embodiment of the present disclosure. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure, but are not intended to limit this disclosure.

[0033] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] To facilitate understanding, before introducing the embodiments of this disclosure, the following explanations are provided for several terms involved in the embodiments of this disclosure:

[0035] Nanochain structure: A linear structure composed of equally spaced nanoparticles (polystyrene PS spheres are used in this invention), used as a functional molecular carrier and signal amplification carrier.

[0036] Detection area: also known as functional area, the chip surface is divided into different detection functional areas, each area is fixed with different targeting antibodies or probes, used to identify specific targets.

[0037] Blank control area: The region in the chip where no functional molecules are fixed, used to eliminate background signal interference and improve detection specificity.

[0038] Multi-target recognition: Parallel recognition of different pathogen recognition sites (such as LPS, Omp, and toxin factors) can be achieved within a single chip.

[0039] This disclosure provides a nanostructured chip for multi-target detection of various microorganisms, as well as its preparation method, detection method, and related equipment. It can simultaneously identify multiple characteristic targets of microorganisms (such as genus-specific genes, species-specific genes, virulence factors, etc.), greatly improving the detection sensitivity of microorganisms.

[0040] It should be noted that the nanostructure chip provided in this embodiment can be used to identify any kind of microorganism. For ease of description, Vibrio is used as an example to illustrate each embodiment.

[0041] Vibrio are an important group of Gram-negative bacteria, widely distributed in marine and freshwater environments. Some species are highly pathogenic, posing a serious threat to human health. Major pathogenic strains include Vibrio parahaemolyticus, Vibrio vulnificus, and Vibrio cholerae. These Vibrio species can infect humans through the consumption of seafood or exposure to wounds, leading to foodborne diarrhea, sepsis, severe soft tissue infections, and even outbreaks of cholera.

[0042] Traditional methods for detecting Vibrio include the following:

[0043] (1) Bacterial culture method: The operation is standardized and the results are reliable, but the culture time is long (24–72 hours), which cannot meet the needs of rapid response.

[0044] (2) Molecular biological detection (such as polymerase chain reaction PCR, quantitative polymerase chain reaction qPCR, loop-mediated isothermal amplification LAMP, etc.): These methods have high sensitivity and can identify genes at the gene level, but they depend on laboratory conditions, and sample processing and operation are complex, making them unsuitable for on-site detection. (3) Immunochromatography or colloidal gold test strips: These methods are easy to operate, but they mainly detect single targets and have low sensitivity, making it difficult to achieve joint identification of bacterial genus, species, and virulence.

[0045] It is evident that current Vibrio detection technologies have significant shortcomings in terms of multidimensional identification capabilities, detection efficiency, system integration, biocompatibility, and field deployability. The nanochain structure chip provided in this embodiment can simultaneously achieve joint identification of genus, species, and virulence status, and has advantages such as high integration, strong stability, multi-target detection, fast response, and adaptability to complex field environments.

[0046] Figure 1 This is a schematic diagram of a nanochain structure chip structure according to an embodiment of this disclosure, as shown below. Figure 1 As shown, the nanochain structure chip includes: a chip substrate 100; and a nanoparticle chain structure 200 fixed on the surface of the chip substrate 100. The nanoparticle chain structure 200 is an equidistant array structure formed by the self-assembly of nanoparticles under the guidance of a microwall template and a surfactant. The nanoparticle chain structure 200 is divided into a genus detection area 201, a species detection area 202, a virulence detection area 203, and a blank control area 204. The genus detection area 201, the species detection area 202, and the virulence detection area 203 are respectively fixed with antibodies or nucleic acid probes with corresponding detection functions.

[0047] It should be noted that when performing highly specific identification of a certain microorganism, the three antibody regions on the nanochain structure chip can detect bacteria sequentially from genus to species to virulence. In practical applications, if it is necessary to perform broad identification of three (or more) bacteria, the three antibody binding regions on the nanochain structure chip can bind to the specific surface protein antibodies of their respective bacteria.

[0048] In some embodiments, the chip substrate 100 may be a silicon wafer.

[0049] In some embodiments, the nanoparticles used to construct the nanoparticle chain structure 200 are polystyrene (PS) nanoparticles; the surfactant is sodium dodecyl sulfate (SDS) solution. In an optional embodiment, a 5 μm pitch microwalled template can be used to guide the mixture of PS nanoparticles and SDS solution to be dropped onto a silicon wafer, and incubated at a first temperature (e.g., 56°C) for a preset time (e.g., 1 hour) to form a regular chain structure. After heat curing at a second temperature (e.g., 115°C), the carboxyl groups on the chain surface are activated using EDC / NHS. Different types of antibodies (e.g., LPS antibodies, OmpU antibodies, TDH antibodies, etc.) or nucleic acid probes are dropped into the genus detection area 201, the species detection area 202, and the virulence detection area 203, respectively, to achieve spatial separation and fixation. Non-specific sites are blocked with 10 mg / mL BSA to reduce background interference.

[0050] Optionally, the PS nanoparticles are 500 nm in size.

[0051] Optionally, in the above mixture containing PS nanoparticles and SDS solution, the concentration of PS nanoparticles is 2 mg / mL; and the concentration of SDS solution is 1 mg / mL.

[0052] It should be noted that, in the embodiments of this disclosure, the shape of the nanochain structure chip may be, but is not limited to, other shapes. Figure 1 The square shown can also be a rectangle of other sizes or other shapes in specific implementations. Nanochain structure chips of different shapes can have different numbers or different detection functions of detection areas, and the arrangement of these detection areas can be arbitrary. For example, in some embodiments, when the shape of the nanochain structure chip is rectangular, the genus detection area, the species detection area, and the virulence detection area can be placed in the first row, and the blank control area can be placed in the second row. The width of the blank control area is equal to the sum of the widths of the genus detection area, the species detection area, and the virulence detection area, so that the genus detection area, the species detection area, and the virulence detection area can be compared with the blank control area respectively.

[0053] In one embodiment, when the shape of the nanochain structure chip is square... Figure 1 The dimensions of the medium-nanochain structure chip are: length l =2.5cm; width w =2.5cm.

[0054] In one embodiment, the spacing between nanoparticle chains d =5μm.

[0055] Furthermore, it should be noted that, in order to achieve joint identification of genus, species, and virulence, the nanochain structure chip needs to include at least four regions: a genus detection region, a species detection region, a virulence detection region, and a blank control region. However, in specific implementations, more detection regions can be arranged on the nanochain structure chip according to different detection needs. This disclosure does not impose specific limitations in this regard.

[0056] In an optional embodiment, when the nanochain structure chip provided in this disclosure is used for Vibrio detection, the objects to be identified in the genus detection area, species detection area, virulence detection area, and blank control area, as well as the antibodies or nucleic acid probes to be immobilized, are shown in Table 1.

[0057] Table 1

[0058]

[0059] It should be noted that the nanochain structure chip provided in this embodiment can be applied to, but is not limited to, the detection of samples such as food extracts, water samples, and clinical secretions. During detection, these samples can be pre-treated (e.g., centrifuged, diluted) and then directly added to the chip surface. Functionalized molecules in each region of the chip bind to target antigens (e.g., LPS, outer membrane proteins, toxins), triggering recognizable signals such as nanochain aggregation, color changes, or enhanced light scattering. Changes in the reaction area are read using an optical microscope or a portable image acquisition system, and the results are output using color analysis and AI image recognition algorithms. The nanoarray appears yellow-green due to its own scattering characteristics. When bacteria are detected, the coupling between the bacteria and the nanoarray modulates the scattering spectrum, changing the color from yellow-green to red (the light source is incident at a 70° angle to the nanoarray plane). Strong red bright spots appear at the binding sites of individual bacteria, and the scattering signal is amplified due to near-field localization effects (increasing the intensity by 2–3 orders of magnitude).

[0060] When the genus detection area, species detection area, and virulence detection area of ​​the nanochain structure chip are respectively fixed with the antibodies or nucleic acid probes shown in Table 1, a positive result in the genus detection area indicates that the sample contains Vibrio spp.; a positive result in the species detection area identifies the specific Vibrio species among pathogenic Vibrio, Vibrio vulnificus, and Vibrio cholerae; the virulence detection area indicates the virulence status. Experiments have shown that the nanochain structure chip provided in this embodiment can complete the detection in 15–30 minutes, making it ideal for rapid on-site determination of the species and virulence status of microorganisms.

[0061] As can be seen from the above, the nanostructure chip provided in this embodiment offers parallel recognition of multiple targets, enabling full-chain recognition from genus to species to virulence; the detection zones on the chip surface are clearly defined with low background interference; specificity is enhanced by setting a blank control zone; the light signal is enhanced by utilizing the nanochain structure, improving antigen binding efficiency and detection sensitivity; a portable detection device can be formed by combining microfluidics and image recognition modules; and the fixed antibodies or probes in each detection zone (also called functional zone) on the chip surface can be replaced according to different detection needs, allowing for the detection of different microorganisms.

[0062] Figure 2 This is a schematic diagram illustrating the principle of a nanochain-antibody immobilization mechanism in an embodiment of this disclosure, as shown below. Figure 2 As shown, polystyrene nanoparticles (500 nm) are activated by EDC / NHS reagent via surface carboxyl groups, forming covalent amide bonds with antibody amino groups. The antibody Fab segment is oriented towards the liquid phase to maximize antigen capture efficiency, and non-specific sites are blocked by 10 mg / mL BSA. This directional immobilization method significantly improves antibody activity retention (>90%) and chip stability (≥15 days at 4°C).

[0063] Figure 3 This is a schematic diagram illustrating the principle of multi-target Vibrio recognition in an embodiment of this disclosure, as shown below. Figure 3 As shown, Vibrio's LPS antigen (target region A), outer membrane proteins (such as OmpU, target region B), and toxins / genes (target region C) bind to immobilized molecules in their corresponding functional regions. Anti-LPS antibodies enhance the signal through multivalent binding, species antibodies target the bacterial species based on epitope specificity, and nucleic acid probes recognize virulence genes based on complementary pairing. Antigen binding induces nanochain aggregation, leading to an increase in local light scattering intensity (visible color change).

[0064] Figure 4 This is a flowchart illustrating the method for detecting microorganisms using a nanochain structure chip in this embodiment of the present disclosure. Figure 4 As shown, the sample was concentrated or diluted by centrifugation and then added to the chip (400μL-600μL sample). The chip was reacted at room temperature for 20 minutes to allow for sufficient antigen-antibody binding. After washing three times with deionized water to remove unbound material, images were acquired using an optical microscope (6000×3984 resolution). The entire process took ≤30 minutes, significantly faster than traditional methods (PCR ≥60 min, incubation ≥24 h).

[0065] Figure 5 This is a schematic diagram illustrating the results of microbial detection using a nanochain structure chip in an embodiment of this disclosure, as shown below. Figure 5As shown, Vibrio positive samples exhibited significant aggregation signals in regions A (genus identification), B (species identification), and C (virulence), while region D remained unchanged. Normal samples showed no color change in regions A, B, and C, and the background in region D was uniform. The lack of change in region D throughout confirms the system's specificity.

[0066] For example, Figure 6 This is an example of a nanochain structure chip structure for detecting pathogenic Vibrio in this disclosure, such as... Figure 6 As shown, when the nanochain structure chip is used to detect pathogenic Vibrio, anti-LPS antibody is immobilized in the genus detection area; anti-OmpU antibody is immobilized in the species detection area; and anti-TDH antibody is immobilized in the virulence detection area.

[0067] For example, Figure 7 This is a nanochain structure chip structure for detecting Vibrio vulnificus in an embodiment of this disclosure, such as... Figure 7 As shown, when the nanochain structure chip is used to detect Vibrio vulnificus, the genus detection area is immobilized with anti-LPS antibody; the species detection area is immobilized with anti-OmpK antibody; and the virulence detection area is immobilized with anti-TDH antibody.

[0068] For example, Figure 8 This is an example of a nanochain structure chip structure for detecting Vibrio cholerae in this disclosure, such as... Figure 8 As shown, when the nanochain structure chip is used to detect Vibrio cholerae, the genus detection area is immobilized with anti-LPS antibody; the species detection area is immobilized with anti-OmpT antibody; and the virulence detection area is immobilized with ctxA complementary probe.

[0069] Based on the same inventive concept, this disclosure also provides a method for fabricating a nanochain structure chip, such as... Figure 9 As shown, the preparation method includes the following steps:

[0070] S902, a mixture containing PS nanoparticles and SDS solution is vertically dropped onto the chip substrate;

[0071] S904 involves depositing a microwalled template on a chip substrate with a drop-on mixture, so that nanoparticles self-assemble into an equally spaced nanoparticle chain structure under the guidance of the microwalled template and SDS solution.

[0072] S906, the chip with the added mixture is placed in a constant temperature chamber, the micro-wall template on the surface of the chip substrate is removed, so that the nanoparticle chain structure is fixed on the surface of the chip substrate, and a nano-chain structure chip is obtained.

[0073] S908 uses an activator solution to treat the nanochain structure chip, and fixes antibodies or nucleic acid probes with corresponding detection functions in the genus detection area, species detection area, and virulence detection area of ​​the nanochain structure chip respectively.

[0074] S910 uses bovine serum albumin (BSA) solution to encapsulate the nanochain structure chip.

[0075] Optionally, in the method for preparing the nanochain structure chip provided in this embodiment, the nanoparticles used are polystyrene (PS) nanoparticles, and the surfactant used is sodium dodecyl sulfate (SDS) solution.

[0076] Based on the same inventive concept, this disclosure also provides a method for detecting nanochain structure chips, such as... Figure 10 As shown, the detection method includes the following steps:

[0077] S102, the test sample is dropped onto the nanochain structure chip, and multiple frames of images are acquired during the reaction process between the nanochain structure chip and the test sample. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area and a blank control area. The genus detection area, species detection area and virulence detection area are respectively fixed with antibodies or nucleic acid probes with corresponding detection functions.

[0078] S104, Perform segmentation processing on each frame of the target image to obtain the corresponding bacterial genus detection area image, bacterial species detection area image, bacterial virulence detection area image and blank control area image for each frame;

[0079] S106a, input the fungal detection area image and blank control area image corresponding to each frame image into the pre-trained fungal detection model, and output the fungal detection result of each frame image;

[0080] S106b: Input the bacterial species detection area image and blank control area image corresponding to each frame image into the pre-trained bacterial species detection model, and output the bacterial species detection result of each frame image;

[0081] S106c, input the virulence detection area image and blank control area image corresponding to each frame image into the pre-trained virulence detection model, and output the virulence detection result of each frame image;

[0082] S108. Correlation analysis is performed on the bacterial genus detection results, bacterial species detection results, and bacterial virulence detection results of multiple frames of images to determine whether the test sample contains microorganisms and the species and virulence of the microorganisms.

[0083] It should be noted that during the reaction between the nanochain structure chip and the detection sample, information such as the color or brightness of the acquired images may change. To avoid experimental errors caused by a single image acquisition, in this embodiment, multiple frames of images are continuously acquired during the reaction between the nanochain structure chip and the detection sample. This reduces measurement errors and allows for more accurate detection results. Since the image feature information required for genus detection, species detection, and virulence detection may differ, in this embodiment, to achieve more accurate detection results, genus detection models, species detection models, and virulence detection models are pre-trained using machine learning to identify the corresponding image regions, thereby obtaining more accurate detection results.

[0084] In some embodiments, multiple frames of images are acquired during the reaction process between the nanochain structure chip and the detection sample, according to set acquisition parameters. Analysis of the image data reveals key information such as the morphological changes of the nanochain structure during the reaction, the reaction rate, and the reaction equilibrium point. This information is crucial for in-depth research into the interaction mechanism and reaction kinetics between the nanochain and the detection sample.

[0085] The acquisition parameters here include, but are not limited to: acquisition start time, acquisition interval, and total acquisition duration. In one embodiment, the acquisition start time is within the first duration (e.g., 0-5 seconds) after the detection sample is dropped onto the nanochain structure chip, the acquisition interval is the second duration (e.g., 0.5-1 seconds), and the total acquisition duration is the third duration (e.g., 1-10 minutes).

[0086] Furthermore, in one embodiment, the acquisition interval is adjusted according to the reaction rate between the detection sample and the nanochain structure chip. When the reaction rate is fast, the acquisition interval is set to a fourth duration (e.g., 0.5 seconds); when the reaction rate is slow, the acquisition interval is set to a fifth duration (e.g., 1 second), and the fifth duration is longer than the fourth duration.

[0087] In this embodiment, by setting reasonable acquisition start time, acquisition interval, and total acquisition duration, and combining a high-resolution (e.g., no less than 10 megapixels) and high-frame-rate (e.g., no less than 10 frames / second) image acquisition device, subtle dynamic changes during the reaction between the nanochain structure chip and the detection sample can be accurately captured. For example, in the specific binding reaction between proteins and nanochains, the real-time change process of nanochains from a dispersed state to an aggregated state can be clearly recorded, capturing the minute morphological changes of local binding sites on the surface of nanochains in the early stage of the reaction. This provides intuitive and accurate image data support for analyzing the reaction mechanism, solving the problem that traditional detection methods are difficult to observe the dynamic process of the reaction in real time.

[0088] Optionally, in some embodiments, the acquired multi-frame images are processed by image denoising, image enhancement, and image segmentation to extract morphological change information of the nanochain structure during the reaction process. Specifically, Gaussian filtering is used for image denoising, histogram equalization for image enhancement, and threshold segmentation for image segmentation. By performing denoising, enhancement, and segmentation on the acquired multi-frame images, noise interference in the images is effectively removed, highlighting the characteristic information of the nanochain structure, improving the quality of the image data, and thus enhancing the accuracy of the detection results based on image analysis.

[0089] To achieve more accurate detection results, multiple image acquisition units can be used to acquire multiple frames of images from multiple angles (e.g., three angles) during the reaction between the nanochain structure chip and the test sample. These multi-frame images from different angles are then processed using a 3D reconstruction algorithm to construct a 3D morphological change model of the nanochain structure during the reaction. This allows for a more comprehensive analysis of the spatial structural changes of the nanochains during the reaction, making it suitable for scenarios requiring high spatial distribution characteristics of the interaction between the nanochains and the test sample, such as the analysis of specific binding sites between viruses and nanochains.

[0090] Furthermore, in some embodiments, in addition to image acquisition, other signal acquisition units can be combined to collect other signal information during the reaction process between the nanochain structure chip and the detection sample. For example, the intensity change of the fluorescence signal during the reaction can be acquired using a fluorescence detector; or, for example, a working electrode, a reference electrode, and a counter electrode can be set on the nanochain structure chip, and the current-voltage curve or impedance change curve during the reaction can be acquired using an electrochemical workstation. During the acquisition process, image, fluorescence signal, and electrochemical signal acquisition are performed simultaneously, and the timestamps of each acquisition parameter are kept consistent, achieving multi-parameter collaborative acquisition. This scheme can simultaneously acquire morphological change information, fluorescence signal change information, and electrochemical signal change information during the reaction process, verifying the accuracy of the detection results from multiple dimensions. It is suitable for fields with extremely high requirements for the reliability of detection results, such as pathogen detection in clinical disease diagnosis. In this embodiment, multi-dimensional detection data such as images, fluorescence, and electrochemistry are acquired simultaneously. Different types of data can mutually verify and supplement each other, avoiding the errors that may exist with a single detection parameter, and significantly improving the reliability of the detection results. For example, in nucleic acid detection, image data can be used to observe the binding morphology of nanochains and nucleic acids, fluorescence data can be used to quantitatively analyze the amount of bound nucleic acids, and electrochemical data can reflect the charge changes during the binding process. The combination of these three factors makes the detection results more accurate and reliable.

[0091] In some embodiments, when acquiring multiple frames of images during the reaction process between the nanochain structure chip and the detection sample, a timestamp corresponding to each frame of image can be acquired. The detection method provided in this embodiment can perform correlation analysis on the genus detection results, species detection results, and virulence detection results of multiple frames of images through the following steps:

[0092] S112a, Based on the acquisition timestamp corresponding to each frame of image, perform time-dimensional color and / or brightness change analysis on the fungal genus detection results of multiple frames of images to obtain the first color and / or brightness change result;

[0093] S112b, based on the acquisition timestamp corresponding to each frame of the image, perform time-dimensional color and / or brightness change analysis on the bacterial species detection results of multiple frames of images to obtain the second color and / or brightness change results;

[0094] S112c, based on the acquisition timestamp corresponding to each frame of the image, perform time-dimensional color and / or brightness change analysis on the virulence detection results of multiple frames of images to obtain the third color and / or brightness change results;

[0095] S114, perform correlation analysis on the first color and / or brightness change results, the second color and / or brightness change results, and the third color and / or brightness change results to determine whether the test sample contains microorganisms and the species and virulence of the microorganisms.

[0096] It should be noted that during the reaction between the nanochain structure chip and the detection sample, the color and / or brightness of the acquired images will change. In this embodiment, the color and / or brightness changes of the genus detection results, species detection results, and virulence detection results corresponding to each frame of the image are first analyzed from the time dimension to obtain the feature information of the time dimension. Then, the correlation analysis of the genus detection results, species detection results, and virulence detection results is performed to realize multi-dimensional analysis and obtain more accurate detection results.

[0097] Optionally, in some embodiments, the collected color parameters include, but are not limited to, RGB or HSV values, and the collected brightness parameters include, but are not limited to, grayscale values. By comparing the color parameters and / or brightness parameters at different time points, the temporal dimension feature information corresponding to the genus detection results, species detection results, and virulence detection results is obtained. The temporal dimension feature information here may include, but is not limited to, the rate of color change and the magnitude of brightness change.

[0098] After obtaining the time dimension features of the genus detection results, the species detection results, and the virulence detection results, correlation analysis is performed to establish the feature mapping relationship between the three. The detection results are then verified and corrected through the feature mapping relationship, and the final microbial detection results are output.

[0099] Furthermore, in some embodiments, before extracting color and / or brightness parameters, each frame of the image is preprocessed. This preprocessing includes image denoising and image cropping. Image denoising employs a bilateral filtering algorithm, and image cropping preserves the effective image region relevant to detection by locating the coordinate range of the nanochain array. When only color change analysis is performed, the color difference value of the target detection region in adjacent frames is calculated, and a curve showing the color difference value changing over time is fitted as temporal feature information. When only brightness change analysis is performed, the brightness difference value of the target detection region in adjacent frames is calculated, and a curve showing the brightness difference value changing over time is fitted as temporal feature information.

[0100] Furthermore, in some embodiments, the correlation analysis of color and / or brightness change results can employ machine learning algorithms (such as random forest algorithm, support vector machine algorithm). The temporal features of the genus detection results, species detection results, and virulence detection results are used as input features, and the microbial detection results are used as output labels. A feature mapping relationship model is trained, and the model output results are used to verify and correct the genus detection results, species detection results, and virulence detection results.

[0101] In this embodiment, color and / or brightness changes in the detection results of genus, species, and virulence are analyzed from a temporal perspective. Compared to traditional detection methods based solely on a single frame image or a single time point parameter, this approach captures dynamic feature information (such as color change rate and brightness change amplitude) during the detection process. By establishing the correlation between the temporal feature information of the genus, species, and virulence detection results, mutual verification and correction of multiple results can be achieved. By extracting color and / or brightness parameters and fitting change curves, the color and / or brightness changes of the image are transformed into quantifiable temporal feature information. This quantified data can not only be used for microbial detection but also provide intuitive experimental evidence for basic research on microbial growth patterns and virulence expression mechanisms, thus broadening the application scenarios of nanochain structure chip technology.

[0102] To achieve automatic recognition and segmentation of acquired images, in some embodiments, the detection method provided in this disclosure can segment each frame of target image through the following steps: inputting each frame of image into a pre-trained nanochain structure chip image segmentation model, and outputting the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame of image, wherein the nanochain structure chip image segmentation model is a pre-trained model used to identify the genus detection area, species detection area, virulence detection area, and blank control area on the nanochain structure chip.

[0103] It should be noted that the output images of the genus detection area, species detection area, virulence detection area, and blank control area all retain the resolution, color, and brightness information of the original target image. Furthermore, each segmented region image includes a corresponding region category identifier and its position coordinates within the original target image. In practice, the output segmented region images can be quality-checked. If a segmented region image has missing regions or blurred edges (blurring exceeding a preset threshold, such as 5%), the corresponding original target image is re-input into the nanochain structure chip image segmentation model for secondary segmentation until a qualified segmented region image is output.

[0104] Optionally, in some embodiments, each frame of the target image is preprocessed before being input into the model. This preprocessing includes, but is not limited to, image normalization, which normalizes the image pixel values ​​to the range of 0-1 to adapt to the input requirements of the nanochain structure chip image segmentation model.

[0105] It should be noted that in practical applications, nanochain structure chips may be divided into more or fewer detection regions, and the shape of the detection regions may be, but is not limited to, [the following]. Figure 1 To quickly achieve automatic segmentation of chip images, in this embodiment of the present disclosure, a nanochain structure chip image segmentation model is pre-trained through machine learning, and the nanochain structure chip image segmentation model is used to automatically identify and segment the acquired nanochain structure chip images to obtain the corresponding bacterial genus detection area image, bacterial species detection area image, bacterial virulence detection area image and blank control area image for each frame.

[0106] In this embodiment, each frame of the target image is input into a pre-trained segmentation model, which automatically outputs segmented images of four key regions without requiring manual region identification and division. Since the segmentation results include blank control area images, the color and / or brightness information of the blank control area can be used as a benchmark during subsequent detection and analysis to eliminate the influence of interference factors such as changes in ambient light and sample impurities on the analysis results of the genus detection area, species detection area, and virulence detection area.

[0107] Figure 12 This disclosure illustrates an image recognition process based on artificial intelligence technology, such as... Figure 12 As shown in the embodiments of this disclosure, the detection method for the nanochain structure chip can input images of the genus detection area, species detection area, virulence detection area and blank control area on the nanochain structure chip into a pre-trained artificial intelligence (AI) model, output the identification result of whether the detection sample contains microorganisms, and when the identification result indicates that the detection sample contains microorganisms, output the species and virulence of the microorganisms.

[0108] In some embodiments, the AI ​​model provided in this disclosure may be, but is not limited to, a convolutional neural network model, a recurrent neural network model, a support vector machine model, etc. Further, in some embodiments, after outputting the recognition results of the detected samples, the AI ​​model may also output various reports with guiding suggestions based on the recognition results. Even further, in some embodiments, it may be combined with a large language model to guide users in performing microbial detection and output guiding suggestion reports based on the detection results.

[0109] It should be noted that the microorganisms in this embodiment can be, but are not limited to, Vibrio. Vibrio is a Gram-negative, facultative anaerobic bacterium that is arc-shaped or comma-shaped. After determining that a bacterium belongs to the genus Vibrio, it is necessary to further identify which specific species within the genus Vibrio. The genus Vibrio contains more than 100 species, common examples being Cholera bacteria, parahaemolytic bacteria, and trauma bacteria. Identifying the specific species of the bacterium provides a foundation for subsequent research (such as pathogenicity analysis and the formulation of prevention and control measures). For example, Cholera bacteria and parahaemolytic bacteria have drastically different transmission routes and pathogenic mechanisms, requiring targeted treatment. After identifying the bacterial species, it is further necessary to test whether the strain carries virulence factors to determine its pathogenicity and the strength of its pathogenicity. Virulence factors are substances produced by bacteria that are related to infection and pathogenicity (such as toxins, adhesion proteins, invasive enzymes, etc.), and the virulence factors vary greatly among different bacterial species. For example, not all Cholera bacteria carry cholera toxin; only virulent strains can cause cholera epidemics. First, the genus is identified through "genus identification," then the specific species is identified through "species identification," and finally, the pathogenicity of the strain is determined through "virulence identification." These three steps are indispensable in food safety (such as detecting pathogens in seafood), clinical diagnosis (such as identifying bacteria in the feces of diarrhea patients), and epidemic prevention and control, collectively forming a complete chain of "classification-tracing-risk assessment" for microorganisms.

[0110] In practice, different AI models can be trained for different microorganisms.

[0111] Based on the same inventive concept, this disclosure also provides a detection device, such as... Figure 13 As shown, the detection device includes: an image acquisition module 131, an image segmentation module 132, an AI detection module 133, and a correlation analysis module 134.

[0112] The image acquisition module 131 is used to drop the test sample onto the nanochain structure chip and acquire multiple frames of images during the reaction process between the nanochain structure chip and the test sample. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively fixed with antibodies or nucleic acid probes for corresponding detection functions. The image segmentation module 132 is used to segment each frame of the target image to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame. The AI ​​detection module 133 is used to: perform genus detection on each frame of the image. The genus detection image and the blank control image are input into a pre-trained bacterial genus detection model, and the bacterial genus detection result of each frame is output. The bacterial species detection area image and the blank control image corresponding to each frame are input into a pre-trained bacterial species detection model, and the bacterial species detection result of each frame is output. The bacterial virulence detection area image and the blank control image corresponding to each frame are input into a pre-trained bacterial virulence detection model, and the bacterial virulence detection result of each frame is output. The correlation analysis module 134 is used to perform correlation analysis on the bacterial genus detection result, bacterial species detection result and bacterial virulence detection result of multiple frames to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms.

[0113] In practice, the aforementioned detection equipment can be any device capable of acquiring and analyzing images; it can be specialized medical equipment or other terminal devices such as mobile phones and computers.

[0114] Based on the same inventive concept, this disclosure also provides a detection system, such as... Figure 14 As shown, the detection system includes: a nanochain structure chip 141 of any of the above and an image acquisition and analysis device 142.

[0115] The nanochain structure chip 141 is used to react with the test sample; the image acquisition and analysis device 142 is used to acquire multiple frames of images during the reaction process between the nanochain structure chip and the test sample; each frame of the target image is segmented to obtain the corresponding bacterial genus detection area image, bacterial species detection area image, bacterial virulence detection area image, and blank control area image; the bacterial genus detection area image and blank control area image corresponding to each frame are input into a pre-trained bacterial genus detection model to output the bacterial genus detection result for each frame; the bacterial species detection area image and blank control area image corresponding to each frame are input into a pre-trained bacterial species detection model to output the bacterial species detection result for each frame; the bacterial virulence detection area image and blank control area image corresponding to each frame are input into a pre-trained bacterial virulence detection model to output the bacterial virulence detection result for each frame; the bacterial genus detection result, bacterial species detection result, and bacterial virulence detection result of multiple frames are correlated to determine whether the test sample contains microorganisms and the species and virulence of the microorganisms.

[0116] It should be noted that the above-mentioned nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes with corresponding detection functions.

[0117] It should be noted that the aforementioned image acquisition and analysis device 142 can be implemented by a single device (such as a terminal) or by multiple devices (such as a terminal and a server). The terminal here can be, but is not limited to, mobile phones, tablets, laptops, notebook computers, handheld computers, netbooks, super mobile personal computers, wearable devices, augmented reality devices, virtual reality devices, etc. This disclosure does not limit the specific type of terminal. The server can be a server that provides various services, such as a backend management server that supports the device operated by the user using the terminal. The backend management server can analyze and process received requests and other data, and feed the processing results back to the terminal device. Optionally, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0118] In some embodiments, images of the reaction between the rice chain structure chip and the test sample can be acquired by a local device on-site, and the acquired images can be analyzed to determine whether the test sample contains microorganisms, as well as the species and virulence of the microorganisms. In other embodiments, images of the reaction between the rice chain structure chip and the test sample can be acquired by a local device on-site and uploaded to a backend server. The backend server can then analyze the acquired images to determine whether the test sample contains microorganisms, as well as the species and virulence of the microorganisms, and finally return the image recognition results to the local device on-site. The former enables rapid detection; the latter allows complex image analysis algorithms to be executed on a server to obtain more accurate image analysis results. Executing the image analysis process on a server reduces the requirements for the local device on-site, requiring only that the local device has image acquisition and uploading capabilities.

[0119] For example, when using an AI model to recognize images on the surface of a chip, deploying the AI ​​model on a local device can achieve rapid image recognition, but due to the limited processing power of the terminal, only a relatively small AI model can be deployed on the terminal. However, deploying the AI ​​model on a backend server can take advantage of the powerful processing power of the backend server to train more complex AI models and can update the AI ​​models in real time to obtain more accurate image recognition results.

[0120] The following details the fabrication and detection process of the nanochain structure chip in this embodiment, using pathogenic Vibrio, Vibrio vulnificus, and Vibrio cholerae as examples:

[0121] Example 1: Preparation and detection process of a three-in-one nanochain structure chip for rapid identification of pathogenic Vibrio.

[0122] (1) Experimental objective:

[0123] A nanochain structure chip capable of simultaneously detecting Vibrio genus, specific Vibrio species and their virulence factors was constructed to achieve rapid identification and virulence typing of Vibrio parahaemolyticus.

[0124] (2) Materials and reagents:

[0125] Polystyrene (PS) nanoparticles, 500 nm in diameter; 5 μm spacing microwalled template; silicon wafer substrate (cleaned and pretreated with plasma); anti-Vibrio LPS polyclonal antibody (MyBioSource, catalog number MBS149520); anti-OmpU monoclonal antibody (Genetex, catalog number GTX123456); anti-TDH monoclonal antibody (Abcam, catalog number ab168912); EDC (1-ethyl-3-(3-dimethylaminopropyl)carbodiimide); NHS (N-hydroxysuccinimide); BSA (bovine serum albumin, 10 mg / mL); 0.01 M PBS buffer (pH 7.4); Vibrio parahaemolyticus standard strain (ATCC 17802); microscope (Nikon LV100) and image acquisition system (NIS-Elements).

[0126] (3) Chip fabrication steps:

[0127] Template assembly and nanochain formation: The prepared 5μm pitch microwalled template was attached tightly to the surface of a glass slide, and 3.5μL of mixed ink (2mg / mL PS particles + 1mg / mL SDS) was added by vertical drop method. The slide was incubated in a 56°C incubator for 1 hour, and the template was removed to form a regular nanochain array.

[0128] Thermosetting: The chip is placed in an oven at 115°C and heated for 35 minutes to enhance the stability of the nanochain structure.

[0129] Carboxyl group activation.

[0130] The chip was treated with freshly prepared EDC / NHS solutions (0.2M and 0.1M concentrations, respectively) for 20 minutes to activate the carboxyl groups on the surface of the nanoparticles.

[0131] Antibody fixation of functional regions: Anti-LPS antibody (10 μg / mL) was added to region A; anti-OmpU antibody (10 μg / mL) was added to region B; anti-TDH antibody (10 μg / mL) was added to region C; 100 μL of each was added, and the mixture was incubated at room temperature for 1 hour.

[0132] Blocking treatment: Add 10 mg / mL BSA solution to the entire chip surface and incubate at room temperature for 30 minutes to block non-specific binding sites.

[0133] Chip drying and storage: After washing with deionized water, air dry and store at 4°C away from light for later use.

[0134] (4) Testing steps:

[0135] Sample preparation: Use 1 mL of Vibrio parahaemolyticus culture medium (concentration 1×10⁻⁶). 6 Centrifuge (CFU / mL) and resuspend in PBS buffer to use as a dummy sample.

[0136] Sample loading and incubation: Add 400μL-600μL of sample solution to the center of the chip, gently cover with a transparent cover, and react at room temperature for 20 minutes.

[0137] Washing and drying: Gently wash 3 times with deionized water for 5 seconds each time, then gently blow dry.

[0138] Microscopic image acquisition: Images of each region were acquired under a Nikon LV100 microscope at a resolution of 6000×3984. The images were analyzed and the signal intensity was quantified using NIS-Elements software.

[0139] (5) Results Explanation:

[0140] Area A (LPS channel): Bacteria were observed to be clearly adsorbed and aggregated on the nanochains, and the color change of the nanochains (shifting towards red) indicated the presence of Vibrio spp.; Area B (OmpU channel): A strong positive reaction was observed, indicating Vibrio parahaemolyticus; Area C (TDH virulence factor channel): A positive signal was observed, indicating that the strain has strong virulence; Area D (blank control area): No obvious signal change was observed, indicating that non-specific adsorption was controllable. The entire detection process took approximately 30 minutes, with clear image signals, accurate region identification, and results consistent with the characteristics of the strain, verifying the feasibility and specificity of the platform's structural and functional design.

[0141] Example 2: Fabrication and detection process of a three-in-one nanochain structure chip for Vibrio vulnificus identification.

[0142] (1) Experimental objective:

[0143] The invention verifies the species identification capability of the three-in-one nanochain chip platform in detecting Vibrio vulnificus and further evaluates the differentiation effect of the virulence identification module.

[0144] (2) Materials and reagents:

[0145] Except for the following differences, the remaining materials are the same as in Example 1: anti-OmpK antibody (MyBioSource, catalog number MBS123456); Vibrio vulnificus standard strain (ATCC 27562); the remaining materials are the same as in Example 1.

[0146] (3) The settings of each detection area (also known as the functional area) of the chip are shown in Table 2.

[0147] Table 2

[0148]

[0149] (4) Testing operation steps:

[0150] Chip fabrication: Same as in Example 1, except that region B is fixed with anti-OmpK antibody.

[0151] Sample preparation: Use 1 mL of Vibrio vulnificus liquid culture (1×10⁻⁶) 6 After centrifugation (CFU / mL), the sample was resuspended in PBS to prepare a simulated infection sample.

[0152] Sample loading and reaction: Add 400μL-600μL of sample to the center of the chip and react at room temperature for 20 minutes.

[0153] Cleaning and drying: Rinse gently 3 times with deionized water and air dry.

[0154] Microscopic image acquisition and signal analysis: Same as in Example 1, acquire images of regions A, B, C, and D and analyze signal intensity.

[0155] (5) The test results and analysis are shown in Table 3:

[0156] Table 3

[0157]

[0158] (6) Conclusion:

[0159] This embodiment verifies that the chip platform of the present invention has good regional recognition capabilities in different target channels. The platform successfully achieved genus-level positive identification, accurate species determination, and negative identification of virulence regions for Vibrio vulnificus, demonstrating good target discrimination and specificity. In contrast to Example 1, this chip platform can effectively support parallel identification and preliminary typing of different bacterial species and their virulence differences, possessing high-throughput detection and practical traceability capabilities.

[0160] Example 3: Preparation and detection of a three-in-one nanochain structure chip for Vibrio cholerae identification and virulence gene detection.

[0161] (1) Experimental objective

[0162] This study verifies the feasibility of the chip platform of the present invention in the practical application of a nucleic acid probe detection module for identifying Vibrio cholerae, specific Omp proteins, and the cholera toxin gene (ctxA).

[0163] (2) Materials and reagents:

[0164] Except for the following differences, the rest is the same as in Examples 1 and 2: anti-OmpT antibody (self-made or custom-made, targeting Vibrio cholerae outer membrane protein T); ctxA nucleic acid probe (5'-thiol-C6 modified, sequence reference GenBank); Vibrio cholerae standard strain (V. cholerae O1, ATCC 14035); the sample contains bacterial lysis products after lysis, including free toxins and genes; the remaining materials (nanochain preparation, chip modification reagents, etc.) are the same as before.

[0165] (3) The settings of each detection area (also known as a functional area) are shown in Table 4:

[0166] Table 4

[0167]

[0168] (4) Chip construction and operation steps:

[0169] Chip fabrication: The same nanochain fabrication method as in Example 1 was used, with anti-LPS antibody (10 μg / mL) immobilized in region A; anti-OmpT antibody (10 μg / mL) immobilized in region B; and a 5'-thiol-modified ctxA complementary DNA probe was attached to the surface of the nanochain structure via Au–S bonds in region C.

[0170] Sample preparation: Vibrio cholerae culture medium (1×10⁻⁶) 6 (CFU / mL) is treated with heating and lysis buffer to release toxic proteins and intracellular DNA.

[0171] Chip detection procedure: Add 400μL-600μL of lysis sample solution to the chip surface and incubate at room temperature for 30 minutes. Then wash with deionized water and dry.

[0172] Signal acquisition: Color / nanochain aggregation images are acquired in the LPS and Omp regions. The binding of the ctxA probe induces changes in chain structure stability, and color density changes are identified through optical imaging or by introducing visible signals through spiked nanoparticles.

[0173] The test results and analysis are shown in Table 5.

[0174] Table 5

[0175]

[0176] (5) Conclusion: This embodiment demonstrates that the chip platform of the present invention is not only suitable for protein antigen recognition modules, but also compatible with nucleic acid probe detection modules, successfully achieving simultaneous recognition of Vibrio cholerae and its virulence gene (ctxA). This functional expansion verifies that the platform can be further introduced with the detection capabilities of nucleic acid sequences such as drug resistance genes, luminescent genes, and virulence islands in the future. While ensuring detection sensitivity and specificity, the platform of the present invention has strong target scalability and biomolecular compatibility, making it suitable for a wider range of multi-target pathogen recognition needs.

[0177] In summary, the nanochain structure chip provided in this disclosure, through the organic combination of functionalized nanochain array chip structure design and multi-target partitioning biorecognition mechanism, can provide a rapid detection platform for genus-level screening, species identification, and virulence status of Vibrio. This platform can balance high sensitivity, specificity, multi-dimensional recognition, and field applicability, exhibiting significant structural innovation and technological integration, and possessing licensability and industrialization potential. Through this disclosure, the following technical effects can be achieved, but are not limited to:

[0178] (1) Multifunctional partitioned chip structure design: The chip surface is divided into multiple regions according to function (including at least a broad spectrum detection region, a species identification region, a toxicity identification region, and a blank control region). Each region is independently arranged with a functionalized nanochain array to capture different types of target antigens, which is the basis for realizing parallel recognition of multidimensional information.

[0179] (2) Signal enhancement mechanism based on nanochain structure: Nanochains are formed by polystyrene nanoparticles arranged at regular intervals. They can significantly enhance antigen binding efficiency after antibody or probe immobilization, have good biocompatibility and signal visibility, and significantly improve detection sensitivity.

[0180] (3) The scientific integration of the target combination strategy innovatively integrates three key targets into one platform: genus-level recognition targets: LPS O antigen; species-level recognition targets: outer membrane proteins OmpU, OmpK, OmpT, etc.; virulence recognition targets: toxin proteins or toxin gene products such as TDH, TRH, and ctxA. This target combination can systematically identify the source of infection and its pathogenicity, which is the first of its kind in existing technologies.

[0181] (4) Functional molecule immobilization and chip fabrication method: Antibodies or probes of each functional region are directionally immobilized on the nanochain structure through EDC / NHS or other chemical bonding methods. At the same time, BSA or blocking agents are used to block non-specific binding sites to improve the stability, repeatability and lifespan of the chip.

[0182] (5) On-site rapid testing process design: The sample loading-reaction-washing-reading process is reasonably designed, easy to operate, and the test can be completed within 15-30 minutes. It has good on-site adaptability and user-friendliness.

[0183] The embodiments disclosed herein can be applied to, but are not limited to, the scenarios shown in Table 6:

[0184] Table 6

[0185]

[0186] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above method embodiments. It should also be noted that the above modules, as part of an apparatus, can be executed in a computer system such as a set of computer-executable instructions.

[0187] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0189] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0190] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0191] The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this disclosure. It should be understood that the above descriptions are merely specific embodiments of this disclosure and are not intended to limit the scope of protection of this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting a nanochain structure chip, characterized in that, include: The test sample is dropped onto the nanochain structure chip, and multiple frames of images are acquired during the reaction process between the nanochain structure chip and the test sample. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively fixed with antibodies or nucleic acid probes with corresponding detection functions. Each frame of the target image is segmented to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame. The fungal detection area image and blank control area image corresponding to each frame are input into the pre-trained fungal detection model, and the fungal detection result of each frame is output. The bacterial species detection area image and the blank control area image corresponding to each frame are input into the pre-trained bacterial species detection model, and the bacterial species detection results of each frame image are output. The virulence detection area image and blank control area image corresponding to each frame are input into the pre-trained virulence detection model, and the virulence detection result of each frame image is output. The bacterial genus detection results, bacterial species detection results, and bacterial virulence detection results of the multi-frame images are correlated and analyzed to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms. Each image frame corresponds to a collection timestamp. Correlation analysis is performed on the genus detection results, species detection results, and virulence detection results of the multiple image frames to determine whether the sample contains microorganisms and the species and virulence of the microorganisms. This includes: performing time-dimensional color and / or brightness change analysis on the genus detection results of the multiple image frames based on the collection timestamp corresponding to each image frame to obtain a first color and / or brightness change result; performing time-dimensional color and / or brightness change analysis on the species detection results of the multiple image frames based on the collection timestamp corresponding to each image frame to obtain a second color and / or brightness change result; performing time-dimensional color and / or brightness change analysis on the virulence detection results of the multiple image frames based on the collection timestamp corresponding to each image frame to obtain a third color and / or brightness change result; and performing correlation analysis on the first, second, and third color and / or brightness change results to determine whether the sample contains microorganisms and the species and virulence of the microorganisms.

2. The detection method for the nanochain structure chip as described in claim 1, characterized in that, Each frame of the target image is segmented to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image for each frame, including: Each frame of image is input into a pre-trained image segmentation model for nanochain structure chips, and the corresponding images of the genus detection area, species detection area, virulence detection area, and blank control area are output for each frame. The image segmentation model for nanochain structure chips is a pre-trained model used to identify the genus detection area, species detection area, virulence detection area, and blank control area on the nanochain structure chip.

3. The detection method for the nanochain structure chip as described in claim 1, characterized in that, The nanochain structure chip includes: Chip substrate; The nanoparticle chain structure is fixed on the surface of the chip substrate. The nanoparticle chain structure is an equidistant array structure formed by the self-assembly of polystyrene (PS) nanoparticles under the guidance of a microwall template and sodium dodecyl sulfate (SDS) solution. The nanoparticle chain structure is divided into a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes with corresponding detection functions.

4. The detection method for the nanochain structure chip as described in claim 3, characterized in that, The nanochain structure chip is used to detect pathogenic Vibrio. The genus detection area is immobilized with anti-LPS antibody; the species detection area is immobilized with anti-OmpU antibody; and the virulence detection area is immobilized with anti-TDH antibody.

5. The detection method for the nanochain structure chip as described in claim 3, characterized in that, The nanochain structure chip is used to detect Vibrio vulnificus. The genus detection area is immobilized with anti-LPS antibody; the species detection area is immobilized with anti-OmpK antibody; and the virulence detection area is immobilized with anti-TDH antibody.

6. The detection method for the nanochain structure chip as described in claim 3, characterized in that, The nanochain structure chip is used to detect Vibrio cholerae. The genus detection region is immobilized with anti-LPS antibody; the species detection region is immobilized with anti-OmpT antibody; and the virulence detection region is immobilized with ctxA complementary probe.

7. The detection method for the nanochain structure chip as described in claim 3, characterized in that, The nanochain structure chip was prepared by the following method: A mixture containing PS nanoparticles and SDS solution is vertically dropped onto the chip substrate; A microwall template is deposited on the chip substrate on which the mixture is dropped, so that the PS nanoparticles self-assemble into an equally spaced nanoparticle chain structure under the guidance of the microwall template and the SDS solution. The chip with the added mixture is placed in a constant temperature chamber, and the micro-wall template on the surface of the chip substrate is removed, so that the nanoparticle chain structure is fixed on the surface of the chip substrate, thus obtaining a nano-chain structure chip. The nanochain structure chip was treated with an activator solution, and antibodies or nucleic acid probes with corresponding detection functions were immobilized in the genus detection area, species detection area, and virulence detection area of ​​the nanochain structure chip, respectively. The nanochain structure chip was sealed using bovine serum albumin (BSA) solution.

8. A testing device, characterized in that, include: An image acquisition module is used to drop the test sample onto the nanochain structure chip and acquire multiple frames of images during the reaction process between the nanochain structure chip and the test sample. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area. The genus detection area, species detection area, and virulence detection area are respectively fixed with antibodies or nucleic acid probes with corresponding detection functions. The image segmentation module is used to segment each frame of the target image to obtain the corresponding fungal genus detection area image, fungal species detection area image, fungal virulence detection area image and blank control area image for each frame. The AI ​​detection module is used to: input the genus detection area image and blank control area image corresponding to each frame of the image into a pre-trained genus detection model, and output the genus detection result of each frame of the image; input the species detection area image and blank control area image corresponding to each frame of the image into a pre-trained species detection model, and output the species detection result of each frame of the image; input the virulence detection area image and blank control area image corresponding to each frame of the image into a pre-trained virulence detection model, and output the virulence detection result of each frame of the image. The correlation analysis module is used to perform correlation analysis on the genus detection results, species detection results, and virulence detection results of the multi-frame images to determine whether the detection sample contains microorganisms and the species and virulence of the microorganisms. Each image frame corresponds to a collection timestamp. The correlation analysis module is further configured to: perform time-dimensional color and / or brightness change analysis on the bacterial genus detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a first color and / or brightness change result; perform time-dimensional color and / or brightness change analysis on the bacterial species detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a second color and / or brightness change result; perform time-dimensional color and / or brightness change analysis on the bacterial virulence detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a third color and / or brightness change result; and perform correlation analysis on the first color and / or brightness change result, the second color and / or brightness change result, and the third color and / or brightness change result to determine whether the detection sample contains microorganisms and the species and virulence of the microorganisms.

9. A detection system, characterized in that, include: Nanochain structure chip, used to react with the detection sample; An image acquisition and analysis device is used to acquire multiple frames of images during the reaction process between the nanochain structure chip and the detection sample; each frame of the target image is segmented to obtain the corresponding genus detection area image, species detection area image, virulence detection area image, and blank control area image; the genus detection area image and blank control area image corresponding to each frame are input into a pre-trained genus detection model, and the genus detection result of each frame is output; the species detection area image and blank control area image corresponding to each frame are input into a pre-trained species detection model, and the species detection result of each frame is output; the virulence detection area image and blank control area image corresponding to each frame are input into a pre-trained virulence detection model, and the virulence detection result of each frame is output. The bacterial genus detection results, bacterial species detection results, and bacterial virulence detection results of the multi-frame images are correlated and analyzed to determine whether the detected sample contains microorganisms and the species and virulence of the microorganisms. The nanochain structure chip includes: a genus detection area, a species detection area, a virulence detection area, and a blank control area, wherein the genus detection area, species detection area, and virulence detection area are respectively immobilized with antibodies or nucleic acid probes with corresponding detection functions; Each image frame corresponds to a collection timestamp. The image acquisition and analysis device is further configured to: perform time-dimensional color and / or brightness change analysis on the bacterial genus detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a first color and / or brightness change result; perform time-dimensional color and / or brightness change analysis on the bacterial species detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a second color and / or brightness change result; perform time-dimensional color and / or brightness change analysis on the bacterial virulence detection results of the multi-frame images based on the collection timestamp corresponding to each image frame, to obtain a third color and / or brightness change result; and perform correlation analysis on the first color and / or brightness change result, the second color and / or brightness change result, and the third color and / or brightness change result to determine whether the detection sample contains microorganisms and the species and virulence of the microorganisms.

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