A method, device and equipment for evaluating the interaction between a nano - preparation and cells

Abstract

The present invention discloses a method, device and equipment for evaluating the interaction between a nano - preparation and cells, which relates to the technical field of research on the interaction between nano - preparations and cells. The present invention uses a digital holographic imaging system to obtain holograms of the nano - preparation and cells at different time periods, and then performs numerical diffraction reconstruction on the holograms at different time periods to obtain a true phase distribution map of cells at the nano - scale. Characteristic parameters of the cells are extracted from the true phase distribution map at the nano - scale, and a neural network diffusion model is constructed using the characteristic parameters. The neural network diffusion model is trained with the true phase distribution map at the nano - scale so that the model can accurately display the true dynamic evolution under the interaction between the nano - preparation and cells at the nano - scale, promote the design and development of new nano - drugs, and provide important technical support for the field of nanomedicine.

Description

Technical Field

[0001] The present invention relates to the technical field of research on the interaction between nano - preparations and cells, and particularly relates to a method, device and equipment for evaluating the interaction between nano - preparations and cells. Background Art

[0002] Nanotechnology is an emerging research field in medicine and pharmaceutical science; nano - preparations are pharmaceutical preparations prepared using nanotechnology, and the size or structural unit of their core components is in the nanoscale range (1 - 100 nanometers). Such preparations can directly nano - size drug components or form a system by combining drugs with nano - carrier materials. The main types of their nano - materials include liposomes, polymer micelles, nano - emulsions, nano - crystals, etc.; as an emerging drug delivery system, due to its unique physical and chemical properties, adjustable size, surface functionalization ability and good biocompatibility, it has great application potential in the fields of cancer treatment, gene delivery, vaccine development, etc.; however, although nano - preparations show great potential in laboratory research, the cases of successful transformation into marketed nano - drugs are still extremely limited. The key lies in the lack of a comprehensive understanding of the intracellular behavior, distribution and action mechanism of nano - preparations.

[0003] Currently, the main evaluation methods for studying the interaction between nano - preparations and cells in traditional research include fluorescence microscopy and cryo - transmission electron microscopy; among them, fluorescence microscopy relies on the labeling of nano - particles or cells, and can obtain the position of nano - preparations in cells, thereby revealing their action properties and cytotoxicity; cryo - transmission electron microscopy preserves the sample in a near - native state by quickly freezing the sample, and is a selective method for studying the structure of liposomes and a powerful tool for analyzing core - shell nanoparticles, but it requires more careful sample preparation and is usually not suitable for long - term observation of living cells. However, these two types of research methods usually rely on the labeling of nano - particles or cells, and such labeling may change the physical and chemical properties of nano - particles, interfere with their natural behavior in cells, and may lead to changes in cell functions, thereby resulting in the distortion and uncertainty of experimental results.

[0004] The emergence of digital holographic imaging technology provides a possibility to solve the above problems. Digital holographic imaging technology can simultaneously restore the amplitude and phase distribution of the object light field through two steps of interference recording and diffraction reconstruction, quantitatively obtain and analyze the three - dimensional information of the object, and has a high imaging resolution. However, when analyzing an object, it can often only analyze the three - dimensional information of a static object, and it is difficult to analyze the real - time dynamic evolution under the interaction between nano - preparations and cells. Summary of the Invention

[0005] An embodiment of the present invention provides a method, device, and equipment for evaluating the interaction between a nano - preparation and cells, which can solve the problem in the prior art that it is difficult to analyze the real - time dynamic evolution of the interaction between a nano - preparation and cells.

[0006] An embodiment of the present invention provides a method for evaluating the interaction between a nano - preparation and cells, comprising the following steps:

[0007] Obtain holograms of the nano - preparation and cells at different time periods during the interaction between the nano - preparation and cells;

[0008] Obtain the amplitude image and phase image of the holograms at different time periods; and extract the real - phase distribution region of the cells from the phase image according to the amplitude image to form a phase distribution map of real nano - scale cells; extract the characteristic parameters of the cells from the phase distribution map of real nano - scale cells;

[0009] Construct a neural network diffusion model based on the artificial neural network ANN; perform forward diffusion process and reverse attention mechanism training on the neural network diffusion model using the phase distribution maps of cells at different time periods, and constrain the weights in the forward diffusion process and reverse attention mechanism according to the characteristic parameters to obtain the trained neural network diffusion model;

[0010] Input the phase distribution map of the nano - scale cells to be measured into the trained neural network diffusion model to obtain a cell image with highlighted nano - preparations, and identify the interaction mechanism between the nano - preparations and cells from the cell image.

[0011] Preferably, the acquisition of the holograms of the nano - preparation and cells at different time periods includes:

[0012] Obtain two groups of cells, one containing the nano - preparation and the other not, place the two groups of cells in culture dishes respectively, and horizontally place the culture dishes containing the cells on the sample platform;

[0013] Every 30 seconds, use an off - axis digital holographic system to record the holograms of the nano - preparation and cells in the culture dish to obtain the holograms of the nano - preparation and cells at different time periods.

[0014] Preferably, the acquisition of the amplitude image and phase image of the holograms at different time periods includes:

[0015] Identify the center point of the positive first - order spectrum in the holograms at different time periods, take this point as the center, extract the positive first - order spectrum of the holograms at different time periods, and separate the diffraction terms of the positive first - order spectrum of the holograms at different time periods through inverse Fourier transform IFT;

[0016] Based on the diffraction terms of the positive first-order spectrum in the hologram within different time periods, using the angular spectrum diffraction reconstruction algorithm, the object wavefronts of the nanoformulations and cells in the holograms within different time periods are propagated from the holographic plane to the imaging plane; and during this propagation process, the nanoformulations and cells in the holograms within different time periods are identified, digital focusing is performed on each cell and nanoformulation, and the amplitude images and wrapped phase images of the holograms within different time periods are obtained.

[0017] Preferably, the forming of the phase distribution map of real nano-scale cells includes:

[0018] According to the amplitude images within different time periods, regions corresponding to the real object plane part are extracted from the phase images in the corresponding holograms, and the regions of the extracted plane part are subjected to surface fitting using Zernike polynomials to obtain the phase form corresponding to the phase distortion of the plane part distribution;

[0019] According to the phase form, the fitting surfaces are screened out from the regions of the plane part within different time periods, and aberration compensation is performed to obtain the phase images corresponding to the regions of the real object plane part in the phase images within different time periods;

[0020] The phase images are unwrapped to obtain the phase distribution maps of the cells within different time periods.

[0021] Preferably, the extracting of the characteristic parameters of the cells includes:

[0022] According to the phase distribution maps of the cells within different time periods and using the phase calibration of the standard resolution plate, the characteristic parameters inside the cells are obtained;

[0023] The steps of the phase calibration of the standard resolution plate are as follows:

[0024] Use a camera to take pictures of the calibration plate, project the phase distribution map on the calibration plate with a projector, and at the same time use the camera to take pictures; and change the pose of the calibration plate, and repeat to take multiple pictures of the calibration plate with the projected phase distribution map;

[0025] Use the taken pictures of the calibration plate for camera calibration to obtain the corner position information on each calibration plate picture; obtain the absolute phase images in the x-direction and y-direction on the calibration plate pictures of each projected phase distribution map, and convert the range of the phase images to the image row and column range; obtain the image point coordinates corresponding to each corner according to the corner positions of each picture and the converted phase images, and identify the characteristic parameters inside the cells in the phase images;

[0026] The characteristic parameters include: the projected surface area, sphericity coefficient, perimeter, roundness, volume, refractive index distribution, cell contour gradient, and uncertainty of the cells.

[0027] Preferably, the obtaining of the trained neural network diffusion model includes:

[0028] The phase distribution maps of cells in different time periods are used to train the neural network diffusion model in the forward diffusion process, and during the training process, random initialization constraints are set for the weights of the neural network diffusion model according to the characteristic parameters;

[0029] The phase distribution maps of cells in different time periods are used to train the neural network diffusion model in the reverse attention mechanism. During the training process, the weights of the neural network diffusion model are adjusted by using the characteristic parameters to reduce the loss value during the reverse attention mechanism training, and the weights of the neural network diffusion model are updated according to the loss value to obtain the trained neural network diffusion model.

[0030] Preferably, when identifying the interaction mechanism between the nanoplatform and the cells, it is to judge the degree of damage of the nanoplatform to the cell membrane and the degree of fusion with the cells during the interaction between the nanoplatform and the cells by identifying the amount of the highlighted nanoplatform present at different positions of the cells.

[0031] An embodiment of the present invention further provides a device for evaluating the interaction between a nanoplatform and cells, including:

[0032] An acquisition module, configured to obtain holograms of the nanoplatform and cells at different time periods during the interaction between the nanoplatform and cells;

[0033] A phase unwrapping module, configured to obtain the amplitude map and phase map of the hologram in different time periods; and extract the real phase distribution region of the cells from the phase map according to the amplitude map to form a phase distribution map of real nanoscale cells; extract the characteristic parameters of the cells from the phase distribution map of real nanoscale cells;

[0034] A phase recognition module, configured to construct a neural network diffusion model based on an artificial neural network ANN; use the phase distribution maps of cells in different time periods to train the neural network diffusion model in the forward diffusion process and the reverse attention mechanism, and constrain the weights in the forward diffusion process and the reverse attention mechanism according to the characteristic parameters to obtain the trained neural network diffusion model;

[0035] An evaluation module, configured to input the phase distribution map of the to-be-tested nanoscale cells into the trained neural network diffusion model to obtain a cell image with highlighted nanoplatform, and identify the interaction mechanism between the nanoplatform and the cells from the cell image.

[0036] An embodiment of the present invention further provides an electronic device, including a memory and a processor;

[0037] The memory is used to store a computer program;

[0038] When the processor executes the computer program stored in the memory, it realizes the steps of the method for evaluating the interaction between a nanoformulation and cells as described above.

[0039] The embodiments of the present invention provide a method, device, and equipment for evaluating the interaction between a nanoformulation and cells. Compared with the prior art, the beneficial effects are as follows:

[0040] The present invention uses a digital holographic imaging system to obtain holograms of the nanoformulation and cells at different time intervals, and then performs numerical diffraction reconstruction on the holograms at different time intervals to obtain a true phase distribution map of cells at the nanoscale. Characteristic parameters of the cells are extracted from the true nanoscale phase distribution map, and a neural network diffusion model is constructed using the characteristic parameters. The neural network diffusion model is trained with the true nanoscale phase distribution map so that the model can accurately display the true dynamic evolution under the interaction between the nanoformulation and cells at the nanoscale, promoting the design and development of new nano drugs and providing important technical support for the field of nanomedicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall process of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0042] Figure 2 It is a hologram of 180 nm liposomes of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0043] Figure 3 It is a hologram of red blood cells of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the positive first-order wrapping phase of liposomes of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0045] Figure 5 It is a schematic diagram of the positive first-order wrapping phase of red blood cells of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0046] Figure 6 It is a de-distorted phase map of liposomes of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0047] Figure 7 It is a de-distorted phase map of red blood cells of a method for evaluating the interaction between a nanoformulation and cells provided by an embodiment of the present invention;

[0048] Figure 8The three-dimensional phase diagram of a single liposome for the method for evaluating the interaction between a nano preparation and cells provided by an embodiment of the present invention;

[0049] Figure 9 The three-dimensional phase diagram of a single red blood cell for the method for evaluating the interaction between a nano preparation and cells provided by an embodiment of the present invention. Detailed implementation manners

[0050] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following describes in detail the specific implementation manners of the present invention with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0051] Refer to Figure 1 , an embodiment of the present invention provides a method for evaluating the interaction between a nano preparation and cells, including the following steps:

[0052] Step 1: Use an off-axis digital holographic system to record the holograms of cells and nanoparticles, specifically including:

[0053] ① Take two groups of cells, one containing the nano preparation and the other not containing the nano preparation, and place them in culture dishes respectively. Place the culture dishes containing the cells horizontally on the sample platform, where the group with the nano preparation added is the experimental group and the group without the addition is the control group.

[0054] ② Record the holograms of the cells and the nano preparation in the culture dish every 30 seconds.

[0055] Step 2: Numerically diffract and reproduce the recorded holograms to obtain the quantitative phase diagrams of the cells at each moment, specifically including:

[0056] ① Spatial filtering: Separate the diffraction terms of the hologram spectrum, and extract the positive first-order spectrum in the holograms at different time periods; that is, find the center point of the positive first-order spectrum in the hologram spectrum, take this point as the center, extract the positive first-order spectrum of the hologram, and through inverse Fourier transform, separate the positive first-order diffraction term alone.

[0057] ② Propagate the object light wavefront from the holographic plane to the imaging plane through the angular spectrum diffraction reproduction algorithm.

[0058] ③ Identify and distinguish cells and nanoparticles from the holograms, and perform digital focusing on each cell and nanoparticle to obtain clear amplitude diagrams and wrapped phase diagrams;

[0059] ④Extract the region corresponding to the real object plane part in the phase distribution, perform surface fitting using Zernike polynomials to obtain the distribution form of phase distortion, subtract the fitted surface from the phase distribution, and perform aberration compensation.

[0060] ⑤Unwrap the phase image of the cell to restore the true phase distribution of the cell.

[0061] Step 3: Combine the phase calibration results of the standard resolution plate, and calculate the projected surface area, sphericity coefficient, perimeter, roundness, volume, refractive index distribution, cell contour gradient, and uncertainty of the cell from the phase distribution map of the cell. The specific steps for phase calibration of the standard resolution plate are as follows:

[0062] Use the camera to take pictures of the calibration plate, project the phase distribution map on the calibration plate with the projector, and at the same time use the camera to take pictures; change the pose of the calibration plate and repeat taking multiple pictures of the calibration plate with the projected phase distribution map;

[0063] Use the taken pictures of the calibration plate for camera calibration to obtain the corner position information on each calibration plate picture; obtain the absolute phase maps in the x - direction and y - direction on each picture of the calibration plate with the projected phase distribution map, and convert the range of the phase map to the image row - column range; obtain the corresponding image point coordinates for each corner according to the corner positions of each picture and the converted phase map, and identify the characteristic parameters inside the cell in the phase map.

[0064] Step 4: Through the eight characteristic parameters obtained in Step 3 and the artificial neural network ANN, establish a neural network diffusion model to train a model that can identify whether there is a nano - preparation in the cell, specifically including:

[0065] ①Add labels to the control group and the experimental group respectively. In the experimental group, it is necessary to manually determine whether the cell is invaded by the nano - preparation, and divide these data into a training set and a test set according to a ratio of 7:3.

[0066] ②Construct a diffusion model: Input the phase maps of unlabeled cells and the training set data respectively. This model includes a forward diffusion process and a reverse attention mechanism training process, and the output is the determination result of whether the cell contains a nano - preparation and the cell image with the nano - preparation highlighted. Under the guidance of the input parameters and the guidance of the input cell image, obtain a model, and then test it through the test set until a model with an accuracy rate of more than 95% is obtained.

[0067] During the forward diffusion process of the network diffusion model and the training process of the reverse attention mechanism, the weights of the neural network diffusion model are adjusted using feature parameters. In the training of the forward diffusion process, the weights of the model are randomly initialized, and in the training of the reverse attention mechanism, the weights of the model are adjusted to reduce the loss value during training, and the loss value is used to update the weights of the model again to make the weights of the model reach the optimal state.

[0068] Step Five: Monitor the interaction process between the nanomedicine and cells in real time. Input the feature parameters obtained from the hologram into the model to obtain the judgment result and the cell image with the nanomedicine highlighted, so as to monitor the interaction mechanism between the nanomedicine and cells in real time.

[0069] In a specific experiment, the interaction between polyalkylcyanoacrylate nanoparticles loaded with cabazitaxel and NIH-3T3 mouse fibroblasts was traced using digital holography. Specifically:

[0070] I. Preparation of nanoparticles:

[0071] Polyalkylcyanoacrylate (PACA) nanoparticles loaded with cabazitaxel can be used to treat cancer; such polyethylene glycol nanoparticles are synthesized by emulsion polymerization of an aqueous phase containing cyanoacrylate monomers and an aqueous phase containing hydrochloric acid and polyethylene glycol surfactant.

[0072] II. Cell culture:

[0073] NIH-3T3 mouse fibroblasts ( CRL-1658TM) were used for cell line and cell culture experiments; they were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum, 1 mM pyruvate, and 2 mM glutamine, and passaged 3 times a week. The 5th - 20th passages were used for the nanomedicine interaction experiments.

[0074] III. Nanomedicine-cell interaction experiment:

[0075] The mouse fibroblasts were cultured for 3 - 5 days until the cell density reached 90%, then seeded into 96-well plates at a density of 15,000 cells / well, and co-incubated with 5 mg / mL of PACA loaded with cabazitaxel at 37°C and 5% CO2 for 24 hours.

[0076] IV. Label-free tracer data acquisition of nanomedicine-cell interaction:

[0077] At 0 h, 1 h, 2 h, 4 h, 6 h respectively, and then every 2 h thereafter, a digital holographic imaging system was used to observe the interaction between the nanopreparation and the cells, obtain three-dimensional phase maps of the cells at different times and parameters such as their gradients and uncertainties, and classify them into two categories: the results of cells without nanoparticles and the results of cells with nanoparticles.

[0078] As Figure 2 and Figure 3 shown are respectively the hologram of 180 nm liposomes with nanoparticles and the hologram of red blood cells without nanoparticles; as Figure 4 and Figure 5 shown are respectively the positive first-order wrapped phases extracted from the hologram of 180 nm liposomes with nanoparticles and the hologram of red blood cells without nanoparticles; as Figure 6 and Figure 7 shown are respectively the phase maps obtained by de-distorting the positive first-order wrapped phases from the hologram of 180 nm liposomes with nanoparticles and the hologram of red blood cells without nanoparticles; as Figure 8 and Figure 9 shown are respectively the three-dimensional phase maps of 180 nm liposomes with nanoparticles and red blood cells without nanoparticles. According to the three-dimensional phase maps, various cell characteristic parameters can be obtained.

[0079] V. Establishment of a real-time cell status monitoring model:

[0080] A diffusion model was constructed. Through the forward diffusion process and the reverse attention mechanism training process, under the specified conditions of the input parameters and guided by the input cell images, the denoising process was completed and the judgment result and the cell image with the nanopreparation highlighted were output, thereby obtaining a model that can real-time judge the status of the nanopreparation and the cells and can clearly locate the position of the nanopreparation in the cells.

[0081] The present invention utilizes the advantages of digital holographic microscopy having nanoscale sensitivity to phase, etc., combines with a neural network diffusion model, and constructs a new research strategy for label-free and real-time monitoring of the interaction between nanopreparations and cells; this strategy breaks through the limitations of the existing technology, provides a new tool for the research on the distribution, metabolism and action mechanism of nanopreparations in cells, not only helps to deeply understand the biological action mechanism of nanopreparations, but also will promote the design and development of new nanodrugs and provide important technical support for the field of nanomedicine.

[0082] The embodiment of the present invention also provides a device for evaluating the interaction between a nanopreparation and a cell, including:

[0083] An acquisition module for obtaining holograms of the nanopreparation and the cells at different time periods during the interaction between the nanopreparation and the cells.

[0084] A phase unwrapping module, which is used to obtain the amplitude image and phase image of the hologram in different time periods; and extract the real phase distribution region of the cells from the phase image according to the amplitude image to form a phase distribution map of real nano-scale cells; extract the characteristic parameters of the cells from the phase distribution map of real nano-scale cells.

[0085] A phase recognition module, which is used to construct a neural network diffusion model based on the artificial neural network ANN; perform forward diffusion process and reverse attention mechanism training on the neural network diffusion model using the phase distribution maps of cells in different time periods, and constrain the weights in the forward diffusion process and reverse attention mechanism according to the characteristic parameters to obtain the trained neural network diffusion model.

[0086] An evaluation module, which is used to input the phase distribution map of the nano-scale cells to be measured into the trained neural network diffusion model to obtain a cell image highlighted by the nano-preparation, and identify the interaction mechanism between the nano-preparation and the cells from the cell image.

[0087] An embodiment of the present invention also provides an electronic device, including a memory and a processor.

[0088] The memory is used to store computer programs.

[0089] When the processor is used to execute the computer program stored in the memory, the steps of a method for evaluating the interaction between a nano-preparation and cells as described above are implemented.

[0090] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for evaluating the interaction between a nano - preparation and cells, characterized in that, Comprising the following steps: Obtain holograms of the nanoparticle formulation and cells at different time periods during the interaction between the nanoparticle formulation and the cells; Obtain the amplitude map and phase map of the holograms at different time periods; and extract the true phase distribution region of the cells from the phase map according to the amplitude map to form a phase distribution map of the true nano-scale cells; extract the characteristic parameters of the cells from the phase distribution map of the true nano-scale cells; Construct a neural network diffusion model based on the artificial neural network ANN; perform forward diffusion process and reverse attention mechanism training on the neural network diffusion model using the phase distribution maps of cells at different time periods, and constrain the weights in the forward diffusion process and reverse attention mechanism according to the characteristic parameters to obtain the trained neural network diffusion model; Input the phase distribution map of the nano-scale cells to be measured into the trained neural network diffusion model to obtain a cell image with highlighted nanoparticle formulation, and identify the interaction mechanism between the nanoparticle formulation and the cells from the cell image; The obtaining of the amplitude map and phase map of the holograms at different time periods includes: Identify the central point of the positive first-order spectrum in the holograms at different time periods, take this point as the center, extract the positive first-order spectrum of the holograms at different time periods, and separate the diffraction terms of the positive first-order spectrum of the holograms at different time periods through inverse Fourier transform IFT; Based on the diffraction terms of the positive first-order spectrum in the holograms at different time periods, use the angular spectrum diffraction reconstruction algorithm to propagate the object light wavefronts of the nanoparticle formulation and cells in the holograms at different time periods from the hologram plane to the imaging plane; and during this propagation process, identify the nanoparticle formulation and cells in the holograms at different time periods, perform digital focusing on each cell and nanoparticle formulation to obtain the amplitude map and wrapped phase map of the holograms at different time periods; The obtaining of the trained neural network diffusion model includes: Construct a neural network diffusion model based on the artificial neural network ANN; Perform forward diffusion process training on the neural network diffusion model using the phase distribution maps of cells at different time periods, and perform random initialization constraint setting on the weights of the neural network diffusion model according to the characteristic parameters during the training process; Perform reverse attention mechanism training on the neural network diffusion model using the phase distribution maps of cells at different time periods, adjust the weights of the neural network diffusion model using the characteristic parameters during the training process to reduce the loss value during reverse attention mechanism training, and update the weights of the neural network diffusion model according to the loss value to obtain the trained neural network diffusion model.

2. The method for evaluating the interaction between a nano - preparation and cells according to claim 1, characterized in that, The obtaining of the holograms of the nanoparticle formulation and cells at different time periods includes: Obtain two groups of cells, one containing the nanoparticle formulation and the other not containing the nanoparticle formulation, place the two groups of cells in culture dishes respectively, and horizontally place the culture dishes containing the cells on the sample platform; Every 30 seconds, use an off-axis digital holographic system to record the holograms of the nanoparticle formulation and cells in the culture dish once to obtain the holograms of the nanoparticle formulation and cells at different time periods.

3. The method for evaluating the interaction between a nano - preparation and cells according to claim 1, wherein, The forming of the phase distribution map of the true nano-scale cells includes: Extract the region corresponding to the real object plane part from the phase diagram in the corresponding hologram according to the amplitude diagrams in different time periods, and use Zernike polynomials to perform surface fitting on the extracted region of the plane part to obtain the phase form corresponding to the phase distortion of the plane part distribution. Screen out the fitting surface from the regions of the plane part in different time periods according to the phase form, and perform aberration compensation to obtain the phase images corresponding to the regions of the real object plane part in the phase diagrams in different time periods. Unwrap the phase images to obtain the phase distribution diagrams of cells in different time periods.

4. The method for evaluating the interaction between a nano - preparation and cells according to claim 1, wherein The extraction of the characteristic parameters of the cells includes: According to the phase distribution diagrams of cells in different time periods and using the phase calibration of the standard resolution plate, obtain the characteristic parameters inside the cells. The steps of the phase calibration of the standard resolution plate are as follows: Use a camera to take pictures of the calibration plate, project the phase distribution diagram on the calibration plate with a projector, and at the same time use the camera to take pictures; and change the pose of the calibration plate, and repeat to take multiple pictures of the calibration plate with the projected phase distribution diagram. Use the taken pictures of the calibration plate for camera calibration to obtain the corner position information on each calibration plate picture; obtain the absolute phase diagrams in the x-direction and y-direction on the calibration plate pictures of each projected phase distribution diagram, and convert the range of the phase diagram to the image row and column range; obtain the corresponding image point coordinates for each corner according to the corner positions and the converted phase diagrams of each picture, and identify the characteristic parameters inside the cells in the phase diagram. The characteristic parameters include: the projected surface area, sphericity coefficient, perimeter, roundness, volume, refractive index distribution, cell contour gradient, and uncertainty of the cells.

5. A method for evaluating the interaction between a nano - preparation and cells according to claim 1, characterized in that, When identifying the interaction mechanism between the nanoformulation and the cells, it is to judge the degree of damage to the cell membrane and the degree of fusion with the cells of the nanoformulation during the interaction between the nanoformulation and the cells by identifying the amount of the highlighted nanoformulation present at different positions of the cells.

6. An evaluation device for the interaction between a nano - preparation and cells, using the method for evaluating the interaction between a nano - preparation and cells according to any one of claims 1 to 5, characterized in that, It includes: An acquisition module for acquiring holograms of the nanoformulation and cells in different time periods when the nanoformulation and cells interact. A phase unwrapping module for obtaining the amplitude diagrams and phase diagrams of the holograms in different time periods; and extracting the real phase distribution region of the cells from the phase diagram according to the amplitude diagram to form the phase distribution diagram of real nano-scale cells; extracting the characteristic parameters of the cells from the phase distribution diagram of real nano-scale cells. A phase recognition module for constructing a neural network diffusion model based on the artificial neural network ANN; performing forward diffusion process and reverse attention mechanism training on the neural network diffusion model using the phase distribution diagrams of cells in different time periods, and constraining the weights in the forward diffusion process and reverse attention mechanism according to the characteristic parameters to obtain the trained neural network diffusion model. An evaluation module for inputting the phase distribution diagram of the nano-scale cells to be measured into the trained neural network diffusion model to obtain the cell images with highlighted nanoformulations, and identifying the interaction mechanism between the nanoformulation and the cells from the cell images.

7. An electronic device, characterized in that, It includes: A memory and a processor; The memory is used to store computer programs. When the processor is used to execute the computer program stored in the memory, the steps of a method for evaluating the interaction between a nanoformulation and cells as described in any one of claims 1 to 5 are implemented.

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