Research overview
The text below is the source article’s original abstract under a verified reuse license. cc by
Central nervous system (CNS) diseases, including ischemic stroke (IS), Alzheimer's disease (AD), and Parkinson's disease (PD), are leading causes of global disability and mortality, characterized by progressive neuronal loss and irreversible neural circuit damage. Despite advances in understanding their pathophysiology, therapeutic options remain limited due to the complexity of disease mechanisms and challenges in delivering treatments across the blood-brain barrier (BBB). In this context, astrocyte reprogramming has emerged as a groundbreaking strategy for neural repair. By leveraging the plasticity of astrocytes, researchers have demonstrated the potential to convert these glial cells into functional neurons, offering a novel approach to replenish lost neurons and restore neural function. This review explores the latest advancements in astrocyte reprogramming, focusing on transcription factor-mediated, miRNA-induced, and small molecule-based strategies, as well as the molecular mechanisms underlying this process. We also discuss the therapeutic potential of astrocyte reprogramming in CNS diseases, including IS, AD, PD, and other neurodegenerative disorders, while addressing the challenges and future directions for clinical translation. Through a systematic analysis of recent studies, this review highlights the promise of astrocyte reprogramming as a transformative therapeutic strategy for CNS repair, providing new hope for patients with debilitating neurological conditions.
Scientific assessment
Research question
Assess the question within the original study’s scope.
Methods & sample
Check the source article for study design and sample details.
Key findings
An editorially approved findings summary is not yet published for this record.
Limitations
Generalizability, bias and clinical meaning require separate assessment.
Methods, samples and clinical conclusions are not inferred from bibliographic metadata.