Hepatoma Derived Growth Factor Enhances Oligodendrocyte Genesis from Subventricular Zone Precursor Cells
Bibliographic record
Abstract
Oligodendrocytes, the myelinating cells of the central nervous system (CNS), perform vital functions in neural protection and communication, as well as cognition. Enhanced production of oligodendrocytes has been identified as a therapeutic approach for neurodegenerative and neurodevelopmental disorders. In the postnatal brain, oligodendrocytes are generated from the neural stem and precursor cells (NPCs) in the subventricular zone (SVZ) and parenchymal oligodendrocyte precursor cells (OPCs). Here, we demonstrate exogenous Hepatoma Derived Growth Factor (HDGF) enhances oligodendrocyte genesis from murine postnatal SVZ NPCs in vitro without affecting neurogenesis or astrogliogenesis. We further show that this is achieved by increasing proliferation of both NPCs and OPCs, as well as OPC differentiation into oligodendrocytes. In vivo results demonstrate that intracerebroventricular infusion of HDGF leads to increased oligodendrocyte genesis from SVZ NPCs, as well as OPC proliferation. Our results demonstrate a novel role for HDGF in regulating SVZ precursor cell proliferation and oligodendrocyte differentiation.Summary StatementHepatoma derived growth factor (HDGF) is produced by neurons. However, its role in the central nervous system is largely unknown. We demonstrate HDGF enhances i) oligodendrocyte formation from subventricular zone neural stem cells, and ii) oligodendrocyte precursor proliferation in vitro and in vivo.
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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 | 0.000 | 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".