Urine Particle Fluorescence Detection for Mulberry Body Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately distinguish and extract urinary mulberry bodies, which are indicative of Fabry disease, due to their similarity in appearance to other cellular components and the scarcity of training data for neural networks, making early diagnosis challenging.
Innovation Solution
A particle detection method involving fluorescence imaging with multiple dyes and a series of extraction steps using scattergrams to isolate mulberry bodies based on size, fluorescence patterns, and morphological features, enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual inspection by microscope is used to detect mulberry bodies, then the method is simple and inexpensive, but the detection precision is low due to similarity in form between mulberry bodies, red blood cells, and fungi
Solution Approach 1:
The patent changes the detection parameters from simple visual inspection to multi-parameter fluorescence analysis. By using multiple fluorescent dyes that bind to different cellular components (nucleic acids, red blood cells, lysosomes) and analyzing multiple fluorescence signals simultaneously, the system achieves high precision in distinguishing mulberry bodies from other cells while maintaining automated operation.
Solution Approach 2:
The patent introduces fluorescent dyes as intermediary substances that bind to specific cellular components. These dyes act as mediators between the detection system and the target cells, enabling indirect but highly specific identification of mulberry bodies through their unique fluorescence signature pattern, which resolves the ambiguity of direct visual inspection.
2Reliability
If Gb3 staining is used to identify mulberry bodies, then the method targets the specific lipid accumulation in Fabry disease, but the detection precision is low because Gb3 is also expressed in tubular cells making differentiation difficult
Solution Approach 1:
The patent segments the detection process into multiple independent staining steps, each targeting a different cellular component. Instead of relying on a single Gb3 stain, the system uses separate dyes for nucleic acids, red blood cells, and lysosomes, then integrates the results. This segmentation allows the system to differentiate mulberry bodies from tubular cells by their unique combination of staining patterns rather than relying on ambiguous Gb3 expression alone.
Solution Approach 2:
The patent adds another dimension to the detection by using multiple fluorescent channels with different emission wavelengths. Each dye emits fluorescence at a distinct wavelength, creating a multi-dimensional fluorescence signature space. Mulberry bodies and tubular cells occupy different regions in this multi-dimensional space, enabling precise differentiation even when they share similar Gb3 expression levels.
3Extent of automation
If neural network methods are used for particle detection, then automation is improved, but the detection precision is insufficient due to lack of sufficient training data from rare Fabry disease cases
Solution Approach 1:
The patent creates a self-improving detection system where the automated fluorescence-based method generates its own training data. By accurately detecting and classifying particles using the multi-parameter fluorescence approach, the system automatically accumulates labeled data that can be used to train and refine neural network models, progressively improving performance without requiring external rare disease samples.
Solution Approach 2:
The patent performs preliminary data generation through the fluorescence detection method before neural network training. The automated fluorescence-based particle detection and classification serves as a preliminary step that creates a substantial dataset of labeled particles, which then serves as training data for the neural network, enabling the model to learn from abundant synthetic data rather than relying on scarce clinical samples.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly improves the extraction of mulberry bodies, enabling more precise identification and diagnosis of Fabry disease.
Implementation Method 1
a first fluorescent dye that stains a nucleic acid
Implementation Method 2
a second fluorescent dye that binds to the antibody that specifically binds to the red blood cell
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed is a particle detection method comprising: preparing a sample containing a urine specimen collected from a subject, a first fluorescent dye that stains a nucleic acid, an antibody that specifically binds to a red blood cell, and a second fluorescent dye that binds to the antibody; imaging each of a plurality of particles in the sample, to obtain a fluorescence image; and extracting information regarding which particle obtained from the corresponding fluorescence image is included in a range corresponding to mulberry bodies.