Decoding tissue complexity: multiscale mapping of chemistry-structure-function relationships through advanced...

Area of Science:

• Biomedical Engineering
• Materials Science
• Computational Biology

Background:

• Biological tissues possess intricate multiscale complexity, serving as blueprints for synthetic biomaterials.
• Understanding tissue structure and function is crucial for advancing biological systems knowledge, disease mechanism elucidation, and clinical strategies.

Purpose of the Study:

• To review state-of-the-art biological visualization technologies.
• To emphasize advancements in AI-driven image analysis, multidimensional imaging, and multimodal data integration.
• To explore the future of biological visualization in deciphering tissue complexity and biomaterials development.

Main Methods:

• Systematic review of current biological visualization technologies.
• Analysis of AI, augmented reality, and deep learning integration in imaging.
• Exploration of multidimensional imaging and reconstruction techniques.
• Examination of multimodal data integration strategies.

Main Results:

• Biological visualization technologies bridge macroscopic and molecular insights.
• AI, augmented reality, and deep learning enable real-time, high-resolution, and predictive analyses.
• Multidimensional imaging and multimodal data integration enhance the decoding of tissue complexity.

Conclusions:

• Biological visualization is evolving towards intelligence, multidimensionality, and multiscale precision.
• These advancements provide theoretical and methodological support for biomaterials development.
• The field promises to accelerate breakthroughs in precision medicine, tissue engineering, and therapeutic development.