Regional specification and complementation with non-neuroectodermal cells in human brain organoids
Bibliographic record
Abstract
Along with emergence of the organoids, their application in biomedical research has been currently one of the most fascinating themes. For the past few years, scientists have made significant contributions to deriving organoids representing the whole brain and specific brain regions. Coupled with somatic cell reprogramming and CRISPR/Cas9 editing, the organoid technologies were applied for disease modeling and drug screening. The methods to develop organoids further improved for rapid and efficient generation of cerebral organoids. Additionally, refining the methods to develop the regionally specified brain organoids enabled the investigation of development and interaction of the specific brain regions. Recent studies started resolving the issue in the lack of non-neuroectodermal cells in brain organoids, including vascular endothelial cells and microglia, which play fundamental roles in neurodevelopment and are involved in the pathophysiology of acute and chronic neural disorders. In this review, we highlight recent advances of neuronal organoid technologies, focusing on the region-specific brain organoids and complementation with endothelial cells and microglia, and discuss their potential applications to neuronal diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".