Bibliographic record
Abstract
Neural stem cells (NSCs) in the adult mammalian brain are found in germinal niches where they contribute to tissue homeostasis and neuroplasticity. One of these NSC niches is in the ventricular-subventricular zone (V-SVZ) along lateral ventricles. V-SVZ NSCs are defined by their ability to persist in a quiescent-like state and become activated to generate neurons for olfactory learning or oligodendrocytes for white matter repair. V-SVZ NSCs are also exposed to a plethora of signals in the niche environment that affect their function. How do these adult V-SVZ NSCs integrate information from the many signals in the environment to persist in a non-proliferating state, and become appropriately mobilized to attempt to repair the injured brain? This question is addressed by examining V-SVZ NSCs during two key events, preceding and following the time of mammalian birth, and during a demyelinating injury in the adult brain. The first part of the thesis asks how quiescent-like NSCs emerge from embryonic neural stem cells known as radial precursors (RPs) and describes a molecular mechanism that regulates this transition. LRIG1 was identified a negative regulator of the epidermal growth factor receptor in embryonic cortical RPs and that by inhibiting this pro-proliferative receptor, facilitates the transition of NSCs into the quiescent-like state. The second part of the thesis asks what determines the oligodendrogenic potential of adult V-SVZ NSCs during white matter repair. To address this, single-cell transcriptomic profiling was coupled with lineage tracing to follow the V-SVZ NSC response to a demyelinating injury. This analysis showed that quiescent-like V-SVZ NSCs become activated and undergo increased oligodendrogenesis without changing their transcriptional identity, suggesting that extrinsic cues in the injury environment activate NSCs to promote injury repair. Collectively, these findings highlight the importance of the environment in influencing the V-SVZ NSC fate and identify mechanisms that modulate the NSC response to niche cues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".