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Human mini-brains for reconstituting central nervous system disorders

Lab on a Chip 2023 11 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
You Jung Kang, Yingqi Xue, Jae Hee Shin, Hansang Cho

Summary

This review discussed advances in human brain organoids (mini-brains) for modeling central nervous system disorders, highlighting their potential to overcome limitations of animal models and accelerate drug development for neurological diseases.

Neurological disorders in the central nervous system (CNS) are progressive and irreversible diseases leading to devastating impacts on patients' life as they cause cognitive impairment, dementia, and even loss of essential body functions. The development of effective medicines curing CNS disorders is, however, one of the most ambitious challenges due to the extremely complex functions and structures of the human brain. In this regard, there are unmet needs to develop simplified but physiopathologically-relevant brain models. Recent advances in the microfluidic techniques allow multicellular culture forming miniaturized 3D human brains by aligning parts of brain regions with specific cells serving suitable functions. In this review, we overview designs and strategies of microfluidics-based human mini-brains for reconstituting CNS disorders, particularly Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI), vascular dementia (VD), and environmental risk factor-driven dementia (ERFD). Afterward, the applications of the mini-brains in the area of medical science are introduced in terms of the clarification of pathogenic mechanisms and identification of promising biomarkers. We also present expanded model systems ranging from the CNS to CNS-connecting organ axes to study the entry pathways of pathological risk factors into the brain. Lastly, the advantages and potential challenges of current model systems are addressed with future perspectives.

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