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Bioorthogonal Chemical Imaging of Cell Metabolism Regulated by Aromatic Amino Acids
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Chemical imaging for biological systems: techniques, AI-driven processing, and applications.

Ying Cui1, Zhihan Zhang1, Yuan Shi2

  • 1Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.

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|May 28, 2025
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Summary
This summary is machine-generated.

Chemical imaging advances biological sciences with new platforms and AI. These tools improve disease diagnosis and therapy design, but face challenges in data and standardization.

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Area of Science:

  • Chemical imaging
  • Biological sciences
  • Biomedical research

Background:

  • Innovative chemical imaging platforms have emerged over the past two decades.
  • These technologies enable breakthroughs in disease diagnosis, targeted therapies, and drug resistance understanding.
  • Advancements in resolution, contrast, sensitivity, and speed have transformed the field.

Purpose of the Study:

  • To provide a comprehensive overview of the evolution and current state of chemical imaging technologies.
  • To analyze data processing workflows, including pre-processing and machine learning-assisted pattern extraction.
  • To explore the impact of artificial intelligence (AI) and machine learning on chemical imaging.

Main Methods:

  • Review of innovative chemical imaging platforms (e.g., single-molecule imaging, Raman microscopy, mass spectrometry).
  • Analysis of data processing workflows: pre-processing, machine learning, neural networks.
  • Integration of AI and machine learning for noise reduction, spectral unmixing, and automated feature extraction.

Main Results:

  • AI and machine learning enhance resolution, sensitivity, and data analysis in chemical imaging.
  • Techniques like spectral unmixing and automated feature extraction are transforming biomedical research.
  • Despite progress, challenges remain in dataset quality, generalizability, computational cost, and validation.

Conclusions:

  • Chemical imaging, enhanced by AI, is revolutionizing biological and medical research.
  • Future integration into various biological fields promises unprecedented resolution and speed for studying living systems.
  • Miniaturization and commercialization will broaden accessibility for clinical investigations and in vivo measurements.