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Bioengineer
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Revolutionary AI Technique Unlocks Inner Electrical Signals of Heart Cells from External Readings
- Researchers from University of California San Diego and Stanford have developed an AI-driven, non-invasive method to monitor heart muscle cell activity.
- Traditional techniques that capture electrical signals from heart muscle cells often involve invasive procedures and can damage cells.
- The new method uses an array of nanoscale electrodes to record external electrical signals, which can then be used to reconstruct internal signals through machine learning algorithms.
- This allows for a complete overview of cellular activity, without risking cellular integrity.
- The technique has potential implications for drug development, by allowing scientists to perform screenings on human-derived heart cells.
- The AI-driven method could help reduce the reliance on animal models, speed up drug discovery, and allow for tailored treatment of diseases.
- The research is not limited to cardiology either and has potential for other cell types.
- Researchers hope to refine the AI models to improve intracellular signal reconstruction, and potentially offer predictive models for how drugs affect cardiac tissues.
- The research has been published in Nature Communications.
- This innovation could dramatically reduce the time and costs associated with drug development.
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