3D Bio-Expression System for Neural Network Reconstruction
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Solution Overview
Problem
Current technologies are unable to effectively observe and reconstruct the three-dimensional neural networks in biological tissues, particularly in the brain, due to limitations in optical penetration depth and damage from tissue slicing, which hinders the understanding of gene and protein expression at the cellular level.
Innovation Solution
A bio-expression system that uses a 3D image generating module and virtual reality projecting system to create high-resolution, three-dimensional models of cellular networks, allowing for the visualization of gene or protein expression and connections within tissues, utilizing a process system that includes model-averaging and stereoscopic projection for detailed anatomical and functional analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tissue slicing is used to observe cellular networks, then the structure can be examined, but physical damage occurs and reconstruction is impossible
Solution Approach 1:
The patent extracts only the necessary information (cellular network structure and gene expression data) from the tissue without performing physical slicing. Instead of cutting the tissue into sections, the system uses optical scanning to extract digital information from the intact tissue, thereby avoiding physical damage while still enabling detailed observation and reconstruction of cellular networks.
Solution Approach 2:
The patent creates a digital copy (three-dimensional model) of the cellular networks and gene expression patterns without physically altering the original tissue. By generating virtual representations through optical scanning and computational processing, the system allows repeated analysis of the copied data while the original tissue remains intact and undamaged.
2Ease of operation
If visible light is used for optical observation, then the method is simple, but penetration depth is limited to less than 50 micrometers
Solution Approach 1:
The patent transitions from two-dimensional surface observation to three-dimensional volumetric observation by implementing optical scanning in multiple dimensions. The system scans tissue samples from multiple angles and depths, reconstructing three-dimensional cellular network structures and gene expression patterns, thereby overcoming the depth limitation while maintaining optical method simplicity.
3Adaptability or versatility
If two-dimensional cell culture is used, then the environment is controllable, but it cannot simulate three-dimensional neural networks in the body
Solution Approach 1:
The patent transitions from two-dimensional cell culture observation to three-dimensional in vivo tissue observation. By scanning intact tissue samples and reconstructing three-dimensional models, the system captures the true spatial architecture of neural networks while maintaining the reliability of in vivo environmental conditions, thus resolving the contradiction between dimensional accuracy and physiological relevance.
Data Source
AI summary
The bio-expression system comprising: a process system used to process data and a three-dimension image generating module embedded in the computing system, wherein while input of a set of two-dimensional individual model sections is fed into the process system, the three-dimension image generating module is responsive to the input of the two-dimension individual model sections and is capable of processing an individual model construction and model-averaging procedure, thereby generating an average model for each individual dataset. A database includes a bio-expression sub-database, cellular network sub-database and fine structure sub-database, wherein the database is coupled to the process system to store at least the average model. A stereoscopic projecting system is coupled to the process system to display stereoscopic images for active or passive virtual reality applications, thereby presenting the bio-expressions, cellular networks or bio-fine structures under the input instruction of the process system.