โก Quick Summary
This review explores the ischemic continuum in stroke care, highlighting the importance of dynamic multi-omic biomarkers and AI technologies for personalized treatment. It emphasizes the need for capturing molecular changes over time to improve patient outcomes.
๐ Key Details
- ๐งฌ Focus: Ischemic continuum in stroke care
- ๐ฌ Technologies: Single-cell and spatial multi-omics, proteoform-resolved proteomics, glymphatic imaging
- ๐ค AI Methods: Machine-learning algorithms for data integration and disease trajectory prediction
- ๐ Ethical Considerations: Privacy, access, equity, and fairness in biomarker deployment
๐ Key Takeaways
- ๐ง Stroke treatment has advanced, yet many patients experience partial recovery.
- ๐ Understanding ischemia requires a comprehensive view of molecular and cellular changes.
- ๐ New technologies allow for high-resolution biomarker discovery.
- ๐ Machine learning can predict disease progression trajectories.
- ๐ Global coordination is essential for equitable biomarker-based diagnostics.
- โ๏ธ Ethical issues must be addressed to ensure fair access to new treatments.
๐ Background
Stroke remains a leading cause of disability and death worldwide. Despite advancements in reperfusion therapy, many patients do not achieve full recovery. A significant challenge in stroke care is the inability to monitor the dynamic changes in molecular and cellular programs that occur over time. Understanding these changes is crucial for developing personalized treatment strategies.
๐๏ธ Study
This review synthesizes evidence across the entire ischemic continuum, from acute metabolic failure to immune responses in the nervous system. It organizes findings within a temporal framework that includes three biological compartments: central nervous system tissue, cerebrospinal fluid, and peripheral blood. The authors discuss innovative technologies that enhance biomarker discovery and the integration of multi-modal data.
๐ Results
The review highlights several breakthrough technologies, including single-cell and spatial multi-omics and proteoform-resolved proteomics, which enable researchers to identify biomarkers with unprecedented resolution. Additionally, machine-learning algorithms are showcased for their ability to integrate diverse data sources and predict disease trajectories, offering a promising avenue for personalized stroke care.
๐ Impact and Implications
The findings from this review have significant implications for stroke management. By leveraging advanced technologies and AI, healthcare providers can offer more precise and tailored interventions for stroke patients. This approach not only enhances patient outcomes but also addresses critical ethical considerations surrounding access and equity in healthcare.
๐ฎ Conclusion
This review underscores the transformative potential of multi-omic biomarkers and AI technologies in stroke care. By capturing the dynamic changes in ischemia, we can move towards more personalized and effective treatment strategies. Continued research and global collaboration are essential to ensure that these advancements benefit all patients equitably.
๐ฌ Your comments
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Mapping the Ischemic Continuum: Dynamic Multi-Omic Biomarker and AI for Personalized Stroke Care.
Abstract
Although there have been advancements in stroke treatment (reperfusion) therapy, and it has been shown that many individuals continue to suffer from partial recoveries and continuing decline in their neurological status as a result of suffering a stroke, a primary barrier to providing precise care to patients with stroke continues to be the inability to capture changes in molecular and cellular programs over time and in biological compartments. This review synthesizes evidence that represents the entire continuum of ischemia, beginning with acute metabolic failure and excitotoxicity, and ending with immune response in the nervous system, reprogramming of glial cells, remodeling of vessels, and plasticity at the level of networks, and organizes this evidence in a temporal framework that includes three biological compartments:central nervous system tissue, cerebrospinal fluid, and peripheral blood. Additionally, this review discusses new technologies which enable researchers to discover biomarkers at an extremely high resolution, including single-cell and spatial multi-omics, profiling of extracellular vesicles, proteoform-resolved proteomics, and glymphatic imaging, as well as new computational methods and machine-learning algorithms to integrate data from multiple modalities and predict trajectories of disease progression. The final section of this review will provide an overview of translationally relevant and ethically relevant issues regarding the deployment of predictive biomarkers, such as privacy, access, equity, and fairness, and emphasize the importance of global coordination of research efforts in order to ensure the clinical applicability and global equity of biomarker-based diagnostics and treatments.
Author: [‘Grigorean VT’, ‘Pantu C’, ‘Breazu A’, ‘Oprea S’, ‘Munteanu O’, ‘Radoi MP’, ‘Giuglea C’, ‘Marin A’]
Journal: Int J Mol Sci
Citation: Grigorean VT, et al. Mapping the Ischemic Continuum: Dynamic Multi-Omic Biomarker and AI for Personalized Stroke Care. Mapping the Ischemic Continuum: Dynamic Multi-Omic Biomarker and AI for Personalized Stroke Care. 2026; 27:(unknown pages). doi: 10.3390/ijms27010502