Characterization of the ventricular-subventricular stem cell niche during human brain development
Bibliographic record
Abstract
Human brain development proceeds via a sequentially transforming stem cell population in the ventricular-subventricular zone (V-SVZ). An essential, but understudied, contributor to V-SVZ stem cell niche health is the multi-ciliated ependymal epithelium, which replaces stem cells at the ventricular surface during development. However, reorganization of the V-SVZ stem cell niche and its relationship to ependymogenesis has not been characterized in the human brain. Based on comprehensive comparative spatiotemporal analyses of cytoarchitectural changes along the mouse and human ventricle surface, we uncovered a distinctive stem cell retention pattern in humans as ependymal cells populate the surface of the ventricle in an occipital-to-frontal wave. During perinatal development, ventricle-contacting stem cells are reduced. By 7 months few stem cells are detected, paralleling the decline in neurogenesis. In adolescence and adulthood, stem cells and neurogenesis are not observed along the lateral wall. Volume, surface area and curvature of the lateral ventricles all significantly change during fetal development but stabilize after 1 year, corresponding with the wave of ependymogenesis and stem cell reduction. These findings reveal normal human V-SVZ development, highlighting the consequences of disease pathologies such as congenital hydrocephalus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".