Blood Plasma miRNA Profiling for Endometrial Receptivity Timing
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Solution Overview
Problem
Current methods for determining endometrial receptivity in IVF treatments are invasive, time-consuming, and lack reliability, making it difficult to accurately identify the window of implantation and embryo transfer timing.
Innovation Solution
A method using a blood plasma sample to determine a microRNA expression profile, analyzed by a computer-based model, to classify the endometrial status into pre-receptive, receptive, or post-receptive states, enabling precise timing for embryo transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histological and imaging methods are used to assess endometrial status, then tissue samples can be examined, but the methods are time-consuming and cannot clearly distinguish between receptive and non-receptive states
Solution Approach 1:
The patent replaces traditional mechanical histological examination and imaging methods with a molecular biology-based miRNA expression analysis system. By using blood plasma miRNA profiles and computer-based classification algorithms, the system achieves more accurate and faster endometrial receptivity assessment without requiring time-consuming tissue processing and manual examination.
Solution Approach 2:
The patent introduces miRNA expression profiles as an intermediary biomarker that reflects endometrial receptivity status. Instead of directly examining endometrial tissue, the system uses circulating miRNAs in blood plasma as a proxy indicator, enabling non-invasive and rapid assessment of endometrial state through molecular profiling and computational classification.
2Reliability
If the calendar approach is used to monitor endometrial status, then timing can be estimated, but the method is unreliable for identifying the window of implantation
Solution Approach 1:
The patent replaces the simple but unreliable calendar-based timing method with a sophisticated molecular profiling system. By analyzing miRNA expression patterns in blood plasma and applying computer-based classification, the system provides reliable identification of the window of implantation, transforming an estimation-based approach into a precise diagnostic tool.
Solution Approach 2:
The patent shifts from monitoring temporal parameters (calendar days) to monitoring molecular parameters (miRNA expression levels). By measuring changes in miRNA expression profiles and classifying them into distinct endometrial states, the system provides reliable receptivity assessment that is not dependent on calendar timing but on actual molecular indicators of endometrial readiness.
3Measurement precision
If invasive tissue biopsy methods are used to assess endometrium, then direct tissue analysis is possible, but the procedures are invasive and complex
Solution Approach 1:
The patent uses circulating miRNAs in blood plasma as an intermediary that carries information about endometrial status without requiring direct tissue contact. This allows the system to achieve accurate endometrial receptivity assessment through simple blood draws rather than invasive biopsies, maintaining diagnostic precision while greatly improving patient comfort and procedural simplicity.
Solution Approach 2:
The patent extracts the essential diagnostic information from the endometrium by measuring miRNA expression profiles in circulating blood plasma. Instead of requiring extraction and examination of endometrial tissue itself, the system extracts molecular biomarkers from easily obtainable blood samples, achieving the same diagnostic goal through a non-invasive route.
Data Source
AI summary
The disclosure relates to methods for determining an endometrial status using a sample, for example, a blood plasma sample, from a subject, comprising: (a) performing an assay on the blood sample from the subject to determine a miRNA expression profile, wherein the miRNA expression profile comprises expression levels of a plurality of miRNA and (b) analyzing the miRNA expression profile to obtain a predictive score using a computer-based machine-learning model.