Stem cell‐based region‐specific brain organoids: Novel models to understand neurodevelopmental defects
Bibliographic record
Abstract
The study of human brain development and neurodevelopmental defects has remained challenging so far due to unique, specific, and complex underlying processes. Recent advances in the technologies and protocols of in vitro human brain organoid development have led to immense possibilities of understanding these processes. Human brain organoids are stem-cell derived three-dimensional in vitro tissues that resemble the developing fetal brain. Major advances in stem cell techniques pioneering the development of in vitro human brain development include reprogramming human somatic cells into induced pluripotent cells (iPSCs) followed by the targeted differentiation of iPSCs into the cells of three embryonic germ cell layers. The neural progenitor cells produced by the directed differentiation of iPSCs undergo some level of self-organization to generate in vitro human brain like tissue. A three-dimensional differentiation approach applied to create region-specific brain organoids has successfully led to develop highly specialized cortical, forebrain, pallium, and subpallium in vitro human brain organoid models. These stem cell-based brain organoids are novel models to study human brain development, neurodevelopmental defects, chemical toxicity testing, and drug repurposing screening. This review focuses on the fundamentals of brain organoid development and applications. The novel applications of using cortical organoids in understanding the mechanisms of Zika virus-induced microcephaly, congenital microcephaly, and lissencephaly are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".