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Discerning Amyloid‑β and Tau Pathologies with Learning-Based Quantum Sensing.
Shruti Sundar1,2, Marakkarakath Vadakkepurayil Jabir3, Lukas Glandorf1,2
1Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich 8057, Switzerland.
Quantum entanglement can distinguish between healthy and diseased biological samples. This study used entangled photons and machine learning to detect neurodegenerative disease markers in mice, offering a novel label-free diagnostic tool.
Area of Science:
- Quantum physics and its application in biological sensing.
- Neuroscience and the study of neurodegenerative diseases.
- Biophotonics and label-free diagnostic techniques.
Background:
- Photon entanglement offers unique coherence properties for biological interactions.
- Entanglement decoherence can be exploited to differentiate biological sample states.
- Developing label-free diagnostic tools is crucial for biological and medical research.
Purpose of the Study:
- To demonstrate the use of a polarization-entangled photon source for label-free diagnostics.
- To distinguish between transgenic mouse models of amyloidosis/tauopathy and control strains.
- To explore quantum sensing for neurodegenerative disorder research.
Main Methods:
- Utilized a polarization-entangled photon source to probe biological samples.
- Investigated cortical and hippocampal regions of transgenic and control mouse models.
- Employed a supervised machine learning approach for classification accuracy enhancement.
Main Results:
- Observed greater preservation of entanglement in transgenic disease models compared to controls.
- Achieved reliable distinctions between disease and control groups using machine learning on unseen data.
- Quantum sensing results were validated by confocal imaging.
Conclusions:
- Photon entanglement serves as a viable label-free diagnostic tool for biological samples.
- Quantum sensing demonstrates potential for distinguishing neurodegenerative disease states.
- This approach could advance the study and diagnosis of neurodegenerative disorders.
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