Mapping the Ischemic Continuum: Dynamic Multi-Omic Biomarker and AI for Personalized Stroke Care

Area of Science:

• Neuroscience
• Biomarker Discovery
• Translational Medicine

Background:

• Despite advances in stroke reperfusion therapy, many patients experience incomplete recovery or neurological decline.
• A key barrier to precise stroke care is the difficulty in monitoring molecular and cellular changes over time and across biological compartments.

Purpose of the Study:

• To synthesize evidence on the temporal continuum of ischemic stroke at molecular and cellular levels.
• To organize this evidence within a temporal framework across central nervous system tissue, cerebrospinal fluid, and peripheral blood.
• To discuss emerging technologies and computational methods for biomarker discovery and disease progression prediction.

Main Methods:

• Literature synthesis of evidence covering the stroke continuum from acute failure to long-term neural remodeling.
• Organization of evidence into a temporal framework across three biological compartments.
• Review of advanced technologies like single-cell multi-omics, extracellular vesicle profiling, and glymphatic imaging.
• Discussion of computational and machine-learning approaches for multi-modal data integration.

Main Results:

• Stroke involves a complex cascade from metabolic failure to immune responses, glial reprogramming, vascular remodeling, and network plasticity.
• New high-resolution technologies enable detailed biomarker discovery.
• Advanced computational methods can integrate multi-modal data to predict disease trajectories.

Conclusions:

• Accurate monitoring of molecular and cellular changes is crucial for advancing stroke care.
• Emerging technologies and computational tools hold significant promise for developing predictive biomarkers.
• Ethical considerations and global coordination are essential for equitable clinical application of these advancements.