AI Article Synopsis

  • Ischemic stroke (IS) is a leading cause of disability and mortality, with existing treatments focusing largely on revascularization, which only benefits a small percentage of patients.
  • Recent research emphasizes the importance of the neuro- and thrombo-inflammatory responses following IS, but clinical trials of immunosuppressive therapies have mostly failed due to a lack of understanding regarding inter-patient variability in these responses.
  • Advances in clinical imaging and single-cell technologies, along with machine learning methods, are enhancing our comprehension of the inflammatory processes in IS, potentially helping to predict individual patient outcomes based on their specific inflammatory status.

Article Abstract

Ischemic stroke (IS) is the leading cause of acquired disability and the second leading cause of dementia and mortality. Current treatments for IS are primarily focused on revascularization of the occluded artery. However, only 10% of patients are eligible for revascularization and 50% of revascularized patients remain disabled at 3 months. Accumulating evidence highlight the prognostic significance of the neuro- and thrombo-inflammatory response after IS. However, several randomized trials of promising immunosuppressive or immunomodulatory drugs failed to show positive results. Insufficient understanding of inter-patient variability in the cellular, functional, and spatial organization of the inflammatory response to IS likely contributed to the failure to translate preclinical findings into successful clinical trials. The inflammatory response to IS involves complex interactions between neuronal, glial, and immune cell subsets across multiple immunological compartments, including the blood-brain barrier, the meningeal lymphatic vessels, the choroid plexus, and the skull bone marrow. Here, we review the neuro- and thrombo-inflammatory responses to IS. We discuss how clinical imaging and single-cell omic technologies have refined our understanding of the spatial organization of pathobiological processes driving clinical outcomes in patients with an IS. We also introduce recent developments in machine learning statistical methods for the integration of multi-omic data (biological and radiological) to identify patient-specific inflammatory states predictive of IS clinical outcomes.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10026385PMC
http://dx.doi.org/10.1007/s00281-023-00984-6DOI Listing

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