Bridging the Gap Between Static Histology and Dynamic Organ-on-a-Chip Models

Area of Science:

• Pathology and Pathophysiology
• Biomedical Engineering
• Computational Biology

Background:

• Traditional pathology relies on static morphology (e.g., H&E staining), limiting understanding of dynamic disease processes.
• Current animal models present translational challenges due to interspecies differences.
• A need exists for advanced research frameworks to capture real-time disease dynamics.

Purpose of the Study:

• Introduce "New Pathophysiology," a framework integrating Organ-on-a-Chip (OOC) technology, multi-omics, and AI.
• Overcome limitations of conventional pathology and animal models.
• Enable real-time functional dynamic resolution for disease initiation and progression.

Main Methods:

• Systematic review of over one hundred existing Organ-on-a-Chip (OOC) disease models.
• Analysis of OOC applications in studying pathological processes like inflammation, metabolic dysregulation, and fibrosis.
• Integration of multi-omics and artificial intelligence with OOC technology.

Main Results:

• OOC technology is actively reshaping the study of various organs (kidney, liver, brain) and pathological mechanisms.
• The proposed framework facilitates the reconstruction of disease initiation and progression.
• Mechanism-based classification highlights shared principles across different diseases.

Conclusions:

• New Pathophysiology offers a dynamic, mechanistic approach to disease modeling, moving beyond static morphology.
• Organ-on-a-Chip technology is pivotal in advancing personalized medicine and translational research.
• This framework aims to build upon existing achievements while addressing current limitations in pathology.