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Machine-Learning-Assisted Triple-Gated Raman Enhancement Platform for Selectively Quantifying Lysophosphatidylcholine

Xinwei Huang1, Qingyuan Miao1, Yawei Li2

  • 1Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine Tongji University, Sanmen Road 1279, Hongkou District, Shanghai 200434, China.

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Summary

A novel hybrid interface precisely detects lysophosphatidylcholine (16:0) (LysoPC (16:0)), a key biomarker for aging and cognitive decline. This breakthrough enables accurate lipid quantification for neurometabolic research.

Keywords:
cognitive impairmentlipid recognitionmachine-learningsemiconductorsurface-enhanced Raman scattering

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Area of Science:

  • Biomarker Discovery
  • Metabolomics
  • Nanomaterials

Background:

  • Quantifying structurally similar metabolites in biofluids is challenging.
  • Lysophosphatidylcholine (16:0) (LysoPC (16:0)) is a potential biomarker for aging and cognitive decline.

Purpose of the Study:

  • Develop a high-specificity detection method for LysoPC (16:0).
  • Enable precise quantification of LysoPC (16:0) in biological samples.

Main Methods:

  • Fabrication of a semiconductor-organic hybrid interface (ZrS2@ZrOx-C16) with a triple-gated molecular recognition environment.
  • Integration of phosphocholine-selective coordination, hydrophobic free-energy minimization, and charge-transfer pathways.
  • Utilized machine learning for Raman signal extraction and quantification.

Main Results:

  • Achieved molecular-level discrimination among lysophospholipids with similar structures.
  • Enabled amplified and selective quantitative Raman signals.
  • Demonstrated rapid, label-free quantification of LysoPC (16:0) with R² = 0.999 in human and mouse serum.

Conclusions:

  • Established a framework for precision lipid analytics and high-selectivity metabolic sensing.
  • Enabled precise mapping of LysoPC (16:0) deficits in aging, Alzheimer's disease, and neurocognitive impairment.
  • Provided mechanistic insights into neurometabolic biology.