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Exosome particle size analysis device and method based on deep learning

A particle size analysis and deep learning technology, applied in particle size analysis, particle and sedimentation analysis, analysis of materials, etc., can solve problems such as subjectivity hindering application, avoid cross-contamination problems, avoid sample waste, and avoid the influence of stray light. Effect

Pending Publication Date: 2021-11-23
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Limitations such as the subjectivity of these methods hinder their application in complex scenarios of nanoparticle tracking
In addition, traditional methods need to constantly adjust parameters during use
Especially in complex scenes, the adjustment of parameters is highly subjective and will lead to different results

Method used

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  • Exosome particle size analysis device and method based on deep learning
  • Exosome particle size analysis device and method based on deep learning
  • Exosome particle size analysis device and method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] In this Example 1, a depth study-based exosomal particle diameter analysis device includes:

[0062] The excitation module is configured to form a laser to form an excited light to detect the sample to detect the sample.

[0063] The detection module is configured to transmit the excited light to detect the sample sample to form an exocial side direction;

[0064] Acquisition modules for obtaining video of Exose Brown movements;

[0065] The analysis module is used to combine the image, the positioning of the outer body particles, acquire the exosomal particle motion trajectory, and calculate the particle size of the particles.

[0066] The analysis module includes a positioning unit, a tracking unit, and a computing unit;

[0067] The positioning unit is configured to process the image to obtain positioning information of the image body particles by using a well-trained depth learning network, and the well-trained deep learning network is trained by training. Set of plural...

Embodiment 2

[0089] In this Example 2, a depth study-based primary particle diameter analysis apparatus and method are provided. The laser beam is formed to form a light sheet, and the exotic state particles in the solution are excited in the excitation method, and the low background noise of the outer body is used, and the video is then applied to the particle diameter analysis of the outer body. In the analysis step, first utilize a point diffusion function analog particles scattering images, and use it as a training set training depth learning network for particle positioning. After the positioning, the external secretions were tracked, and the grain size distribution of the exosomal body was obtained using Stokes Aimistan.

[0090] In this Example 2, based on deep learning of the exosomal particle diameter analysis apparatus, including the shaping of the laser, an excitation module (ie excitation module) for performing the sample chip module (detection module) detected by the outer body, F...

Embodiment 3

[0105] In this Example 3, there is provided a depth learning-based particle diameter analysis apparatus and method, using a depth learning method for particle positioning, using a dot diffusion function to simulate the particle image in a complex state, and can adapt to the noise environment. And the nanoparticle positioning in the decociated state, which has higher robustness and accuracy compared to the traditional positioning method. Light illumination excitation technology can effectively limit the excitation area, inhibit background noise, improve image signal-to-noise ratio. Homemade sample chips can be better coupled to the light-illuminated beam to further reduce the influence of stray light on the image, and the cost can be used as a disposable use module to avoid cross-contamination. The present invention can achieve particle observation of small to diameter 41 nm, satisfying the size range of the exosomass measurement and can effectively achieve measurement analysis of ...

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Abstract

The invention provides an exosome particle size analysis device and method based on deep learning, and belongs to the technical field of exosome detection and recognition equipment. The exosome particle size analysis method comprises the steps that: an excitation module shapes laser to form an excitation light sheet which is emitted into a to-be-detected exosome sample; the detection module transmits the exciting light sheet through the exosome sample to be detected to form exosome lateral scattered light; the acquisition module is used for acquiring a video of exosome Brownian motion; then the analysis module is combined with the video and utilizes a deep learning algorithm to position the exosome particles, so that the motion trail of the exosome particles is obtained and the particle size of the particles is calculated. According to the method, background interference and stray light influence are avoided, and the signal-to-noise ratio is relatively high; the cost is low, the cross contamination problem and sample waste are avoided, excessive and overestimation of the particle size cannot be caused, and large-size magnetic beads do not need to be marked, so that marking steps and cost are simplified; therefore, the particle positioning precision is improved, continuous parameter adjustment is not needed, the influence of subjective factors is reduced, and the robustness and applicability of the result are ensured.

Description

Technical field [0001] The present invention relates to a technical field detection exosomes recognition apparatus, particularly relates to a particle analyzing apparatus and method of exosomes based on the depth of learning. Background technique [0002] Exosomes containing vesicle protein and nucleic acid, size of between 40-200 nm, and which may be a variety of cell types secreting cell lines, such as tumor cells, stem cells and neural cells, most widely present in such blood, urine fluid, ascites fluid and the like environment. [0003] Exosomes play in important role in various biological processes, which are containing proteins, lipids, nucleic acids and other biologically active molecules having sugar corresponding function when transferred to a recipient cell, cell-to-cell communication plays a important role, capable of interacting or into an adjacent cell cycle regulation distant cells. Tumor cell-derived exosomes may be performed by fibroblasts, normal cells, immune ce...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06T7/11G06T7/00G06N3/02G06K9/62G01N15/02
CPCG06T7/0012G06T7/11G06T7/20G06N3/02G01N15/0211G06T2207/30024G06F18/214Y02A90/10
Inventor 苏绚涛王卓
Owner SHANDONG UNIV
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