Imagine if we could predict mental health struggles by looking at how hormones change during puberty or pregnancy, rather than just waiting for symptoms to appear. This work uses AI and biological samples to find the 'red flags' in our genes and blood. It's like creating a personalized weather forecast for the brain to prevent storms before they hit.
Current mental health diagnoses rely on subjective symptoms, leading to inaccurate treatments and missed opportunities for early prevention during high-risk hormonal shifts.
The project is developing validated biological markers and AI-driven drug target discoveries to identify mental health risks across 4 life stages.
If you are a drug discovery firm dealing with high failure rates in psychiatric drug trials — this project developed drug target discoveries using AI and multi-OMICs that allow for more precise, sex-specific medication design.
If you are a medical testing laboratory dealing with a lack of objective tests for depression and anxiety — this project developed validated biomarkers that provide biological criteria for diagnosis instead of relying only on patient interviews.
If you are a FemTech app developer dealing with generic health advice for women — this project developed recommendations for early detection during peripartum and puberty stages that can be integrated into personalized wellness tracking.
Based on available project data, no pricing or cost information is provided as the project is in the research and validation phase.
Based on available project data, the project is currently focusing on data alignment and cohort analysis, meaning it is not yet at an industrial scale.
Based on available project data, specific licensing terms are not mentioned, though the project aims to provide recommendations for drug design strategies.
The project period runs from 2023-01-01 to 2027-12-31, suggesting that validated results will emerge toward the end of 2027.
The project aims to complement current symptom-based diagnoses with biological criteria, allowing for more accurate and personalized preventive measures.
The consortium is heavily academic, consisting of 13 universities and 4 research institutions across 8 countries. With 0 industry partners and 0 SMEs, the project is currently driven by scientific discovery rather than commercial application, indicating a high potential for future licensing opportunities as it moves toward validation.
Contact Uppsala Universitet regarding the Re-MEND project outcomes.
Contact SciTransfer to identify potential licensing opportunities from this academic consortium.
