Gene Expression Skin Sampling for CTCL Differential Detection
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Solution Overview
Problem
Existing methods struggle to accurately distinguish between skin cancer, particularly cutaneous T-cell lymphoma (CTCL), and other skin disorders or conditions based on gene expression profiles, often leading to misdiagnosis due to heterogeneity in molecular changes and similar symptoms.
Innovation Solution
A method involving the use of diagnostic models to analyze gene expression data from skin samples, specifically targeting genes such as FYB, ITK, IL26, STAT5A, TRAF3IP3, TNFSF11, CXCL8, CXCL9, CXCL10, and TNF, to differentiate between CTCL and other skin conditions by detecting elevated or down-regulated gene expression levels using adhesive skin sample collectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing diagnostic methods are used to detect skin conditions, then the detection process is simple, but the accuracy of distinguishing between skin cancer and other skin disorders is insufficient
Solution Approach 1:
The diagnostic system segments the skin condition detection task by analyzing specific gene expression profiles (FYB, ITK, IL26, STAT5A, TRAF3IP3, TNFSF11, CXCL8, CXCL9, CXCL10, TNF) separately and systematically. Each gene is evaluated for its expression level and pattern, allowing the system to distinguish between CTCL and other skin disorders through multi-parameter analysis rather than relying on a single diagnostic marker.
Solution Approach 2:
The patent introduces an intermediary computational model that processes gene expression data and generates diagnostic predictions. This intermediary layer translates complex molecular data into actionable diagnostic information, enabling accurate distinction between skin cancer and benign conditions without requiring direct complex interaction between the diagnostic system and the patient.
2Reliability
If gene expression analysis is performed to distinguish CTCL from other skin conditions, then diagnostic accuracy improves, but the complexity of sample collection and analysis increases
Solution Approach 1:
The adhesive skin sample collector is designed to perform self-service by passively collecting skin cells through adhesion during normal skin turnover. The collector requires no active patient participation, no specialized equipment, and no complex procedural steps - it simply adheres to the skin surface and collects cells as they naturally shed, making the sample collection process as simple as applying and removing a adhesive patch.
Solution Approach 2:
The patent replaces complex mechanical biopsy procedures with a passive adhesive-based collection system. Instead of requiring surgical incisions, forceps, or other invasive mechanical methods to obtain skin samples, the system uses molecular adhesion forces to collect cells, significantly simplifying the collection process while maintaining adequate sample quality for gene expression analysis.
3Measurement precision
If multiple genes are analyzed to differentiate skin conditions, then differentiation accuracy increases, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary action by pre-establishing reference gene expression profiles for both CTCL and non-CTCL skin conditions. These reference datasets are created in advance through comprehensive analysis of known cases, allowing the diagnostic system to quickly compare new samples against established patterns rather than performing complex real-time analysis, thereby reducing diagnostic time while maintaining high differentiation precision.
Data Source
AI summary
Disclosed herein, in certain embodiments, are systems and methods of detecting the presence of a skin condition using a machine learning model based on molecular risk factors. In some instances, the skin condition is cancer, such as cutaneous T cell lymphoma (CTCL). In some cases, the skin cancer can be mycosis fungoides (MF) or Sézary syndrome (SS).