Optical metasurface chip for unmarked colorimetric analysis of pathological tissue slice

Abstract

The invention discloses an optical metasurface chip for label-free colorimetric analysis of pathological tissue slices. The optical metasurface chip comprises a chip substrate and a two-dimensional periodic nanostructure on the substrate. The two-dimensional periodic nanostructure can generate a structural colored light signal under the irradiation of white light, the light signal can be directly observed under a common optical microscope, and the color of the light signal is changed along with the change of the refractive index of a biological tissue slice attached to the surface of the light signal, so that the label-free colorimetric analysis of tumor tissue types and differentiation grades is realized. The method does not need dyeing or expensive equipment, is simple and convenient to operate and rapid in analysis, and is suitable for intraoperative rapid diagnosis and fine pathological evaluation of various cancers such as liver cancer and intestinal cancer.

Description

Technical Field

[0001] This invention relates to the fields of nano-optical sensing, imaging and disease diagnosis technology, specifically to an optical metasurface chip for label-free colorimetric analysis of pathological tissue sections. Background Technology

[0002] Optical microscopy, with its advantages of non-invasiveness, real-time processing, and rapid feedback, has played a crucial role in studying disease pathogenesis, morphology, function, and metabolism, becoming a primary tool for the diagnosis of most diseases, especially tumors. However, traditional optical microscopes are limited by the diffraction limit of light, making it difficult to directly observe the fine nanoscale structures within tissues. Furthermore, most biological tissues exhibit strong light absorption and scattering characteristics, further limiting the resolution of optical imaging. Currently, clinical practice primarily relies on colored dyes or fluorescent probes to process and label biological tissue samples to visualize tissue and cellular structures; for example, hematoxylin-eosin (H&E) staining and immunohistochemical staining are the "gold standard" for microscopic tissue examination in histopathology. However, these traditional methods are not only complex, time-consuming, and prone to errors, but may also cause irreversible damage to the samples. Therefore, there is an urgent need to develop efficient and convenient label-free optical real-time imaging technologies for biological tissues to provide reliable technical support for the early screening, diagnosis, and prognostic assessment of diseases such as cancer. Nanophotonic structures, with their excellent light field manipulation capabilities and high degree of structural design freedom, offer a new paradigm for designing high-resolution, multifunctional, and ultra-compact biosensor devices. In particular, artificially designed periodic nanophotonic metasurfaces can generate high-quality optical resonance and significant near-field enhancement, exhibiting extremely high sensitivity to changes in the local refractive index, composition, and structure of biological samples such as cells and tissues. This holds promise for advancing next-generation bioimaging and medical diagnostic technologies. Existing nanophotonic metasurfaces largely rely on complex fabrication processes such as electron beam etching, which are costly and difficult to scale up. In contrast, this invention utilizes a printing self-assembly process, resulting in low cost and mass production capability, overcoming the limitations of complex fabrication methods in existing technologies. Summary of the Invention

[0003] To address the problems of complex, time-consuming, and expensive equipment-dependent staining of pathological tissue sections in existing technologies, this invention provides an optical metasurface chip for label-free colorimetric analysis of pathological tissue sections. This chip features simple operation and immediate diagnostic capability, enabling rapid differentiation of tumor tissue and its degree of differentiation.

[0004] The first aspect of the present invention provides an optical metasurface chip, comprising a chip substrate and a two-dimensional periodic nanostructure printed on the substrate. The metasurface chip is capable of generating light signals that can be directly observed under an optical microscope under white light irradiation. The light signals are used to realize label-free optical imaging and colorimetric analysis of biological tissue sections to rapidly diagnose the type and degree of differentiation of tumor tissues.

[0005] In this embodiment of the invention, the chip substrate material includes silicon substrate, glass substrate, quartz substrate, PDMS substrate, PET substrate, etc.

[0006] In this embodiment of the invention, the two-dimensional periodic nanostructure includes nanoparticles, nanopores, nanodisks, nanopillars, and nanofolds.

[0007] In this embodiment of the invention, the two-dimensional periodic nanostructure has a nanostructure arrangement period of 400 nm to 1000 nm.

[0008] In this embodiment of the invention, the two-dimensional periodic nanostructure has a nanostructure arrangement lattice including a tetragonal lattice and a hexagonal lattice.

[0009] In this embodiment of the invention, the two-dimensional periodic nanostructure has the spectral characteristic of producing at least one narrow-band resonance peak in the visible light range.

[0010] In this embodiment of the invention, the optical signal of the optical metasurface chip is an optical image captured by an optical microscope in any mode such as bright field, dark field, reflection, or transmission.

[0011] In this embodiment of the invention, the optical microscope is a common commercial microscope with an objective lens having a magnification of 20x and an imaging device with a resolution of 16 megapixels. Alternatively, it could be a portable imaging device with equivalent performance, such as a mobile phone.

[0012] In this embodiment of the invention, the biological tissue sections include frozen sections or paraffin sections, and the section thickness is 3 μm to 10 μm.

[0013] In this embodiment of the invention, the colorimetric analysis method includes: (1) Acquire RGB optical images of the optical metasurface chip; (2) Separate the RGB optical image into three channels: red, green, and blue; (3) Identify the core pixel region in each channel and extract its grayscale value; (4) Calculate the average gray value of the core pixel region and compare the optical signal differences between the optical metasurface chip and the normal tissue slice and the cancer tissue slice to determine the type and degree of differentiation of cancer.

[0014] In this embodiment of the invention, the optical metasurface chip is characterized in that the optical signal of the structure can distinguish between tumor tissue and normal tissue.

[0015] In this embodiment of the invention, the optical metasurface chip is characterized in that the structured light signal can distinguish different degrees of differentiation and tumor stages of tumor tissue.

[0016] In this embodiment of the invention, the tumor tissue includes liver cancer tissue, intestinal cancer tissue, gastric cancer tissue, pancreatic cancer tissue, and other tumor tissues that require pathological analysis.

[0017] In a second aspect, the present invention provides a preferred method for fabricating the optical metasurface chip, characterized by comprising the following steps: (1) Align the left edge of the substrate with the left edge of the scraper blade; (2) Drop nanoparticle suspension ink onto the left edge of the substrate and adjust the height of the scraper so that the ink is rolled into a liquid film with a thickness of 100 μm to 200 μm; (3) The high-precision motor driven by the software controls the scraper to move at a constant speed along the substrate to complete the scraping and coating, thereby forming an optical metasurface chip with a two-dimensional periodic nanostructure on the substrate.

[0018] In this embodiment of the invention, the substrate is any one of glass, silicon wafer, quartz, PET or PDMS.

[0019] In this embodiment of the invention, the substrate is characterized by having a two-dimensional periodic structure on its surface, including two-dimensional periodically arranged nanopores, nanogrooves, and nanofolds.

[0020] In this embodiment of the invention, the nanoparticles include polystyrene nanoparticles, silica nanoparticles, silicon nanoparticles, selenium nanoparticles, and cuprous oxide nanoparticles.

[0021] In this embodiment of the invention, the nanoparticles are nanoparticles of various shapes such as spheres, rods, and cubes.

[0022] In this embodiment of the invention, the size of the nanoparticles is 150 nm to 500 nm.

[0023] In this embodiment of the invention, the concentration of the nanoparticle suspension ink is 0.1 mg / mL to 1 mg / mL.

[0024] In this embodiment of the invention, the moving speed of the scraper is 0.001 mm / s to 0.01 mm / s.

[0025] In this embodiment of the invention, the period of the two-dimensional periodic nanostructure is 400 nm to 1000 nm.

[0026] In this embodiment of the invention, the preparation method is characterized by further including a drying step after the coating is completed, drying at room temperature for 30 minutes to remove solvent and enhance the bonding force between nanoparticles and substrate, thereby forming a stable optical metasurface chip.

[0027] In this embodiment of the invention, the optical metasurface chip obtained by the preparation method is characterized in that the chip has a specific resonance peak in the visible light band, making it suitable for biosensing and imaging applications.

[0028] A third aspect of the present invention provides a rapid, label-free imaging method for pathological tissues based on optical metasurface chips, characterized by comprising the following steps: (1) Place unstained pathological tissue sections directly on the surface of the optical metasurface chip; (2) Place the optical metasurface chip with the pathological tissue slice attached in step (1) under an optical microscope and use a 10×, 20× or 50× objective lens to perform bright field imaging to obtain an optical image; (3) Perform colorimetric analysis on the optical image obtained in step (2), extract the values ​​of the three channels R, G, and B, and identify the tissue type and pathological differentiation grade by color difference; (4) The color feature data is compared with the pre-established pathological image database, and the auxiliary diagnosis results are output through the artificial intelligence model.

[0029] In this embodiment of the invention, the thickness of the pathological tissue sections is 3 μm to 10 μm, and no staining treatment is required.

[0030] In this embodiment of the invention, the optical microscope is a common white light transmission or reflection microscope, which does not require fluorescence, confocal or electron microscopy equipment.

[0031] In this embodiment of the invention, the RGB value extraction is achieved by image processing software, such as ImageJ, including region segmentation and average RGB calculation of the image.

[0032] In this embodiment of the invention, the color difference reflects the differences in the concentration, composition and content of proteins in biological tissues, thereby distinguishing normal tissues from cancerous tissues.

[0033] In this embodiment of the invention, the artificial intelligence model is a convolutional neural network (CNN), which is trained using a dataset of labeled optical images of pathological tissues.

[0034] In this embodiment of the invention, the established pathological image database includes standard RGB feature values ​​or optical images of tissue samples from organs such as the intestine, breast, lung, and liver at different differentiation levels.

[0035] In this embodiment of the invention, the rapid label-free pathological tissue imaging method is characterized in that the entire detection process takes no more than 10 minutes. It can quickly determine whether the pathological tissue is normal or cancerous, and can also determine the specific type of cancer, such as distinguishing between hepatocellular carcinoma and intrahepatic cholangiocarcinoma in liver cancer.

[0036] According to an embodiment of the present invention, the pathological section imaging method can not only see the morphology of the tissue, but also the different colors displayed by different parts of the tissue based on different material compositions. In addition to being used for rapid intraoperative pathological diagnosis, it can also be applied to scenarios such as visualization of protein distribution in biological tissues and visualization of anisotropy of fibrous tissue structures.

[0037] The core inventive concept of this invention is "an optical metasurface chip for label-free colorimetric analysis of pathological tissue sections". The optical metasurface chip (two-dimensional periodic nanostructure), the preparation method (printed self-assembly), and the diagnostic application (pathological analysis of tissue sections) all revolve around this core concept and have directly related technical features.

[0038] This invention provides an optical metasurface chip for label-free colorimetric analysis of pathological tissue sections. Using this optical metasurface chip, label-free colorimetric analysis of pathological tissue sections can be performed directly, avoiding traditional hematoxylin and eosin (H&E) and immunohistochemical staining steps, significantly simplifying the pathological analysis process. This chip can replace traditional glass slides and be integrated into existing optical microscope pathological analysis workflows, enabling pathological tissue analysis by observing the color of the optical image. It is particularly suitable for rapid intraoperative pathological assessment scenarios. Attached Figure Description

[0039] Figure 1 This is a schematic diagram and optical photograph of the process for preparing an optical metasurface using self-assembled selenium nanoparticles in Example 1 of the present invention.

[0040] Figure 2 This is a flowchart of the label-free pathological section colorimetric analysis based on metasurface in Embodiment 2 of the present invention.

[0041] Figure 3 This is an optical image of hepatocellular carcinoma tissue analyzed using selenium optical metasurface and traditional H&E staining techniques in Embodiment 3 of the present invention.

[0042] Figure 4 This is an RGB scatter plot and distribution map of hepatocellular carcinoma tissue using selenium optical metasurface imaging in Embodiment 4 of the present invention.

[0043] Figure 5These are the optical images of colorectal cancer tissue obtained by using selenium optical metasurface imaging in Embodiment 5 of the present invention, and the RGB scatter plot and distribution map of colorimetric analysis of label-free pathological sections.

[0044] Figure 6 The images are optical images of normal liver tissue, hepatocellular carcinoma, and intrahepatic cholangiocarcinoma on a selenium supersurface, as well as optical images after staining with H&E, Arg-1, CK19, and MUC-1, and RGB scatter plots of colorimetric analysis, from Example 6 of this invention.

[0045] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. It should be understood that the following embodiments are merely illustrative and explanatory of the present invention, and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above content of the present invention are covered within the scope of protection intended by the present invention.

[0046] Unless otherwise stated, the raw materials and reagents used in the following examples are commercially available products or can be prepared by known methods.

[0047] The optical microscope used in the following examples is a xenon lamp with a wavelength of 400-760 nm. Example

[0048] Preparation of selenium metasurfaces: Figure 1 (a) is a schematic diagram of the process for preparing selenium metasurfaces by blade coating, in which a suspension of 20 μL of 1 mg / mL selenium nanospheres is dropped onto a 1×1 cm⁻¹ plate. 2 A two-dimensional hexagonal array of selenium nanospheres was obtained on a polydimethylsiloxane (PDMS) substrate with an inverse opal structure using a blade-coating self-assembly method. The selenium nanospheres had a diameter of 420 nm and a refractive index of 2.7; the period of the hexagonal array (i.e., the distance between the centers of adjacent selenium nanospheres) was 700 nm.

[0049] Figure 1 (b) is a macroscopic photograph of the prepared selenium metasurface, with a scale bar of 1 cm. The selenium metasurface was observed under an optical microscope, as shown... Figure 1 (c) shows optical images of the selenium metasurface at objective magnifications of 20x and 100x. At 20x magnification, the metasurface displays a uniform red color, while at 100x magnification, the selenium nanospheres are arranged in a hexagonal array. The numerical apertures at objective magnifications of 20x and 100x are 0.4 and 0.9, respectively, with image scales of 20 μm and 2 μm. The camera images the light received by the objective lens at a resolution of 24 megapixels. Example

[0050] This embodiment refers to Example 1 to obtain a selenium metasurface. This embodiment, for example... Figure 2As shown, the label-free pathological slide colorimetric analysis process based on metasurface is as follows: Taking the label-free pathological slide colorimetric analysis of liver cancer tissue as an example, the liver cancer tissue to be analyzed during surgery is approximately 1.0 cm in length, width, and height. After being flash-frozen in liquid nitrogen for 30 seconds, it is embedded in OTC and then frozen into frozen sections using a cryostat. Five sections are continuously sliced ​​at the largest cross-section, with a section thickness of 5 μm. One section is mounted on a selenium metasurface and can be placed under an optical microscope for pathological analysis without further treatment. The remaining four continuous sections are mounted on a glass slide and stained with H&E, Arg-1, CK19, and MUC-1, respectively. H&E staining takes approximately 30 minutes, while immunohistochemical staining takes 1-2 days. Only after staining is completed can pathological analysis be performed under an optical microscope, serving as a verification and supplement to the label-free pathological slide colorimetric analysis. Example

[0051] This embodiment describes the analysis of label-free pathological sections of hepatocellular carcinoma tissue. A selenium metasurface was obtained according to Example 1, and label-free colorimetric analysis of the pathological sections was performed according to the procedure in Example 2. Specifically, hepatocellular carcinoma tissue and adjacent normal tissue were obtained from surgery; each tissue was approximately 0.5 cm in length, width, and height. After being rapidly frozen in liquid nitrogen for 30 seconds, they were embedded in OTC solutions and then cryostats were used to prepare frozen sections. Two sections were cut consecutively at the largest cross-section, with a section thickness of 5 μm. One section was mounted on the metasurface and observed directly under an optical microscope without staining. The objective lens had a magnification of 20x and a numerical aperture of 0.4, and an optical image was captured using a camera. The other section was mounted on a glass slide, stained with H&E, and observed after approximately 30 minutes, with an optical photograph taken at the same location. Figure 3 As shown, the first row contains H&E staining images and selenium supersurface imaging images of normal tissue at the same location, while the second row contains H&E staining images and selenium supersurface imaging images of cancer tissue at the same location. The scale bar for both images is 100 μm. Morphologically, in the H&E staining images, normal tissue appears lamellar with lightly stained nuclei; cancer tissue exhibits a cord-like structure with deeply stained nuclei. On the selenium supersurface, normal tissue also shows the same lamellar structure, with indistinct orange nuclei; cancer tissue also exhibits a cord-like structure, with orange nuclei appearing darker. This indicates that the selenium supersurface can visualize the morphological characteristics of tissue sections without staining and can display differences consistent with H&E staining images. Furthermore, in terms of color, the H&E staining images of normal and cancer tissues show little difference, while the images on the selenium supersurface show normal tissue as orange-red and cancer tissue as large areas of white. Observing the color of the images allows for rapid differentiation between normal and cancer tissues; this embodiment forms the basis of the colorimetric analysis of this invention. Example

[0052] This example demonstrates colorimetric analysis of label-free pathological sections of hepatocellular carcinoma tissue. Referring to Example 3, optical images of frozen sections of normal liver tissue and hepatocellular carcinoma tissue on a selenium supersurface were obtained under a 20x objective lens. Fifty tissue samples from each category were used, and 150 optical photographs of each type were taken under an optical microscope. The average intensity of the red, green, and blue channels in each optical photograph was extracted using ImageJ image processing software. Then, Origin was used to statistically analyze the intensity distribution of the red, green, and blue channels in the photographs. Figure 4 As shown in (a), the RGB values ​​of normal liver tissue and hepatocellular carcinoma tissue were used as coordinates in the XYZ dimensions to draw a three-dimensional RGB distribution map. The points of normal liver tissue and hepatocellular carcinoma tissue showed significantly different and almost non-overlapping distributions, proving that it is feasible to distinguish normal liver tissue and hepatocellular carcinoma tissue by image color. Figure 4 As shown in (b), further, Python was used to extract the intensity values ​​of the red, green, and blue channels of each pixel in the optical image, and then Origin was used to plot the intensity distribution of the three channels. The average values ​​and distribution widths of the three channels for normal liver tissue and hepatocellular carcinoma tissue were significantly different, with the green and blue channels showing the greatest difference. Therefore, the metasurface-based label-free pathological section colorimetric analysis method of this invention can distinguish between normal and cancerous tissue sections based solely on the color differences of tissue sections under an optical microscope, achieving rapid and convenient colorimetric analysis of pathological sections during surgery, without the need for staining. Example

[0053] This embodiment describes the label-free colorimetric analysis of a large number of optical images of colorectal cancer tissue sections. Referring to Example 3, optical images of frozen sections of normal intestinal tissue and colorectal cancer tissue on a selenium supersurface were obtained under a 20x objective lens. Fifty tissue samples from each category were used, and 1500 optical photographs of each type were taken under an optical microscope. The average intensity of the red, green, and blue channels in each optical photograph was extracted using ImageJ image processing software. Then, Origin was used to statistically analyze the intensity distribution of the red, green, and blue channels in the photographs. Figure 5 As shown in (a), the RGB values ​​of normal intestinal tissue and colorectal cancer tissue were used as coordinates in the XYZ dimensions to draw a three-dimensional distribution map of RGB. The coordinate points of normal intestinal tissue and colorectal cancer tissue showed a non-overlapping distribution, proving that normal intestinal tissue and colorectal cancer tissue can be distinguished by image color. Figure 4As shown in (b), further, Python was used to extract the intensity values ​​of the red, green, and blue channels of each pixel in the optical image, and then Origin was used to plot the intensity distribution of the three channels. The average values ​​and distribution widths of the three channels for normal intestinal tissue and colorectal cancer tissue were significantly different. Therefore, it can be seen that the metasurface-based label-free pathological section colorimetric analysis method of this invention is not only applicable to the colorimetric analysis of pathological sections of liver cancer tissue, but can also be extended to the colorimetric analysis of pathological sections of other cancer tissues. Example

[0054] This embodiment demonstrates the use of a label-free colorimetric analysis method based on optical metasurfaces to achieve precise differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Following Example 2, serial frozen sections of normal liver tissue, hepatocellular carcinoma tissue, and intrahepatic cholangiocarcinoma tissue were obtained. One section was mounted on the metasurface, and the remaining four serial sections were mounted on glass slides and stained using H&E, Arg-1, CK19, and MUC-1, respectively. Figure 6 As shown in (a), serial sections were observed using an optical microscope at 20x magnification, and in-situ control optical photographs were taken at the same location on each section. It can be seen that normal tissue appears orange-red on the supersurface, hepatocellular carcinoma tissue appears orange with less white, and intrahepatic cholangiocarcinoma tissue appears largely white, showing significant color differences. Simultaneously, similar to the results of H&E staining, the morphology of the three also shows considerable differences: normal tissue shows a lamellar structure, hepatocellular carcinoma tissue shows a cord-like structure, and intrahepatic cholangiocarcinoma tissue shows an irregular glandular structure and an infiltrative state with abundant mucin secretion. Immunohistochemical staining results showed that hepatocellular carcinoma tissue was deeply stained by Arg-1, while intrahepatic cholangiocarcinoma tissue was deeply stained by CK19 and MUC-1. Further, referring to Example 3, optical images of frozen sections of normal liver tissue, hepatocellular carcinoma tissue, and intrahepatic cholangiocarcinoma tissue on the selenium supersurface were obtained under a 20x objective lens, with 10 samples of each type, resulting in 50 optical photographs for each. The average intensity of the red, green, and blue channels in each optical photograph was extracted using the image processing software ImageJ. Then, Origin was used to statistically analyze the intensity distribution of the red, green, and blue channels in the photograph. For example... Figure 6 As shown in (b), the RGB values ​​of normal liver tissue, hepatocellular carcinoma tissue, and intrahepatic cholangiocarcinoma tissue were used as coordinates in the XYZ dimensions to create a three-dimensional RGB distribution map. The coordinate points of the three tissues show clear distinctions, indicating that color-based differentiation is accurate. Therefore, the method of this invention can achieve precise differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma through colorimetric analysis, without the need for combined determination using multiple staining methods. The scale bar of the optical image is 100 μm.

[0055] The above description is merely illustrative and does not constitute any limitation on the present invention. Any person skilled in the art can make any simple modifications, equivalent changes, and alterations to the technical solutions of the present invention using the disclosed methods and techniques without departing from the scope of the present invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. An optical metasurface chip for label-free pathological tissue imaging, characterized in that, The device includes a substrate and a two-dimensional periodic nanostructure formed on the substrate. The optical metasurface chip can generate a structural color light signal under white light irradiation. This light signal can be directly observed under a conventional optical microscope and changes with the refractive index of the biological tissue slices attached to its surface, thereby enabling label-free colorimetric analysis of tumor tissue type and differentiation grade.

2. The optical metasurface chip as described in claim 1, characterized in that, The chip substrate includes silicon substrate, glass substrate, quartz substrate, PDMS substrate, and PET substrate, etc.

3. The optical metasurface chip as described in claim 1, characterized in that, The two-dimensional periodic nanostructure is composed of nanoparticles, nanopores, nanodisks, nanopillars or nanofolds arranged in a period of 400 nm to 1000 nm, and the lattice type is tetragonal or hexagonal. Furthermore, the scattering, reflection or transmission spectrum of the two-dimensional periodic nanostructure has at least one narrowband optical resonance peak in the visible light band (400 nm to 800 nm). Optionally, the structural color light signal of the two-dimensional periodic structure includes optical images captured by an optical microscope in any mode such as bright field, dark field, reflection, or transmission.

4. The method for fabricating an optical metasurface chip as described in any one of claims 1-3, characterized in that, Includes the following steps: (1) Align the left edge of the substrate with the left edge of the scraper blade; (2) Drop nanoparticle suspension ink onto the left edge of the substrate and adjust the height of the scraper so that the ink is rolled into a liquid film with a thickness of 100 μm to 200 μm; (3) The high-precision motor driven by the software controls the scraper to move at a constant speed along the substrate to complete the scraping, thereby forming the two-dimensional periodic nanostructure on the substrate. (4) The substrate after the coating is dried to remove the solvent and enhance the bonding force between the nanoparticles and the substrate, forming a stable optical metasurface; Optionally, the substrate is characterized by having nanostructures such as two-dimensional periodically arranged nanopores, nanogrooves, and nanofolds on its surface. Optionally, the substrate undergoes surface treatment before the nanoparticle suspension is added, including plasma cleaning, silanization treatment, etc. Optionally, the nanoparticles include polystyrene nanoparticles, silicon nanoparticles, selenium nanoparticles, titanium dioxide nanoparticles, etc. Optionally, the nanoparticles may have various shapes, such as spheres, rods, and cubes. Optionally, the size of the nanoparticles is 150 nm to 500 nm; Optionally, the concentration of the nanoparticle suspension ink is 0.1 mg / mL to 1 mg / mL; Optionally, the scraping speed of the scraper is 0.001 mm / s to 0.01 mm / s; Optionally, the optical metasurface chip obtained by the preparation method is characterized in that the two-dimensional periodic nanostructure has a specific resonance peak in the visible light band (400 nm ~ 800 nm), which is suitable for biosensing and imaging applications.

5. A rapid label-free colorimetric analysis method for pathological tissues using the optical metasurface chip according to any one of claims 1-3, characterized in that, Includes the following steps; (1) Place unstained pathological tissue sections directly on the surface of the optical metasurface chip; (2) Use a regular optical microscope with a 10×, 20× or 50× objective lens to perform bright-field imaging and obtain color optical images; (3) Perform colorimetric analysis on the optical photographs taken, and determine the tissue type and tumor differentiation grade based on the color differences; Optionally, the pathological tissue sections include frozen sections or paraffin sections with a thickness of 3 μm to 10 μm, and no staining treatment is required; Optionally, the microscope is a common white light transmission or reflection microscope, without fluorescence or confocal functions; the imaging device includes a commercial microscope equipped with a camera with a resolution of 16 megapixels or higher, or a portable device with equivalent imaging capabilities.

6. The method as described in claim 5, characterized in that, The pathological tissues mentioned include biological tissues that require pathological analysis, such as liver tissue, intestinal tissue, and bile duct tissue.

7. The method as described in claim 5, characterized in that, The color difference reflects the differences in the composition or distribution of various substances in the pathological tissue, and can be used to distinguish normal tissue from cancerous tissue, as well as tumor tissue of different differentiation grades. Optionally, the differences in the composition of the substances include differences in the types of substances, such as nucleic acids, proteins, and fats, which have different refractive indices, as well as differences in the content of the substances, including differences in concentration and degree of aggregation.

8. The method as described in claim 5, characterized in that: Different pathological tissue sections exhibit color differences visible to the naked eye on the metasurface; alternatively, colorimetric analysis of the acquired images using ImageJ software can distinguish between different pathological tissues. Optionally, the colorimetric analysis specifically includes the following steps: (1) Acquire optical images of the pathological tissue sections to be analyzed on the optical metasurface chip; (2) Use image processing software to extract the intensity values ​​of the three channels of red (R), green (G) and blue (B) of the region to be analyzed in the optical image, and identify the tissue type and pathological differentiation grade by intensity difference; (3) The color feature data is compared with the pre-established pathological image database, and the auxiliary diagnostic results are output through the artificial intelligence model; Optionally, through the colorimetric analysis, the optical metasurface chip can distinguish different parts in the same tissue slice, including hepatocytes, hepatic sinusoids, bile ducts, etc. in liver tissue slices, and can also distinguish the pathological features of different tissue slices, including whether they are cancerous, the type of cancer and the degree of differentiation, etc. The entire diagnostic process does not exceed 10 minutes. Optionally, the image processing software includes ImageJ or other free software capable of analyzing the RGB values ​​of an image; Optionally, the artificial intelligence model is a convolutional neural network (CNN), trained using a specific dataset of optical images of pathological tissues; Optionally, the constructed colorimetric database for specific pathological tissues specifically includes the following steps: (1) The pathological tissue obtained during the operation was made into serial sections, one of which was attached to the metasurface chip and the rest was attached to a glass slide; (2) Perform standard H&E staining and immunohistochemical staining on the histopathological sections attached to the slide in (1) above; (3) Place the stained tissue pathology sections that were attached to the slide in (2) above under an optical microscope and use a 10×, 20× or 50× objective lens for bright field imaging, and use a camera to collect optical photos to obtain pathological analysis results. (4) Place the pathological tissue sections attached to the metasurface in (1) above without staining directly under an optical microscope and use a 10×, 20× or 50× objective lens for bright field imaging, and use a camera to collect optical photos and extract the RGB values ​​of the photos. (5) Based on the pathological analysis results and RGB values ​​of a large number of the same tissues obtained in (4) above, classify them according to the pathological results in (3), statistically analyze the average RGB values ​​and distributions of various pathological results, and establish a color feature database and a corresponding pathological image database. Optionally, the established pathological image database includes standard RGB feature values ​​or optical images of tissue samples from various organs such as the intestine, breast, lung, and liver at different differentiation levels. Optionally, the rapid cancer diagnosis method includes both quickly determining whether a pathological tissue section is normal or cancerous tissue, and determining the type and degree of differentiation of cancer, such as distinguishing between hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

9. The pathological section imaging method as described in claims 5-8, in addition to the rapid cancer diagnosis application as described in claim 8, the optical metasurface chip can also be applied to applications such as visualization of protein distribution and visualization of anisotropy of fibrous tissue structure.

Images