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Constructing Built-In Electric Field in Hierarchical-Flower Heterostructure for High-Performance Serum Metabolic

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Fe3O4/MoS2 nanoparticles enhance laser desorption ionization mass spectrometry (LDI-MS) for serum metabolic profiling. This novel matrix improves lung cancer diagnosis by detecting key metabolites with high accuracy.

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Area of Science:

  • Materials Science: Synthesis and characterization of hierarchical Fe3O4/MoS2 nanostructures.
  • Analytical Chemistry: Application of novel matrices in laser desorption ionization mass spectrometry (LDI-MS).
  • Biomedical Engineering: Development of diagnostic tools for early cancer detection.

Background:

  • Traditional organic matrices for LDI-MS suffer from low sensitivity and high background noise, limiting small molecule detection.
  • Heterostructures offer enhanced charge carrier mobility and photothermal conversion, beneficial for LDI-MS.
  • Serum metabolic profiling (SMP) is crucial for clinical diagnosis, but requires sensitive and reproducible analytical methods.

Purpose of the Study:

  • To synthesize Fe3O4/MoS2 nanoparticles with a hierarchical-flower heterostructure for use as a novel LDI-MS matrix.
  • To enhance the detection sensitivity and reproducibility of serum metabolic profiling using the novel nanomatrix.
  • To apply the developed LDI-MS platform for the early diagnosis of lung cancer through SMP analysis.

Main Methods:

  • Facile synthesis of Fe3O4/MoS2 nanoparticles with hierarchical-flower heterostructure.
  • Utilizing the synthesized nanoparticles as a matrix for LDI-MS analysis of serum samples.
  • Employing machine learning models for distinguishing lung cancer patients from healthy controls based on SMPs.
  • Identifying potential cancer biomarkers through metabolite analysis.

Main Results:

  • The Fe3O4/MoS2 nanomatrix significantly improved LDI-MS performance, exceeding conventional matrices by over an order of magnitude.
  • High reproducibility was achieved, with over 90% of metabolic features showing relative standard deviations below 30%.
  • The platform successfully distinguished lung cancer patients from healthy controls and identified two key metabolites with an AUC of 0.824.

Conclusions:

  • Fe3O4/MoS2 nanoparticles serve as a highly efficient nanomatrix for LDI-MS, enhancing desorption and ionization processes.
  • The developed LDI-MS platform enables sensitive and reproducible serum metabolic profiling from minimal sample volumes.
  • This approach shows significant potential as a powerful tool for early lung cancer diagnosis.