Neural Stem/Progenitor Cells Differentiate In Vitro to Neurons by the Combined Action of Dibutyryl cAMP and Interferon-γ
Bibliographic record
Abstract
Transplantation of neural stem/progenitor cells (NSPCs) is a promising strategy for repair of the diseased/injured central nervous system (CNS); however, controlling their differentiation remains a significant hurdle. This study is aimed at controlling differentiation and specifically at screening exogenous factors to direct NSPC differentiation into neurons in vitro. In this study, adult rat SVZ-derived NSPCs were treated with several factors and screened individually and in combination for changes in cellular morphology, neuronal marker expression, quantitative real-time qRT-PCR, and electrophysiological properties. These in vitro screens showed that of all the different treatments, dibutyryl cyclic AMP (dbcAMP) and interferon-gamma (IFN-gamma) enhanced neuronal differentiation most significantly compared to the 1% fetal bovine serum (FBS) controls. Importantly, the combined treatment of NSPCs with dbcAMP and IFN-gamma promoted greater neuronal differentiation as reflected by an increase in beta-III tubulin expression and morphological differentiation. Interestingly, the neurons that were generated from the NSPCs in vitro in the presence of dbcAMP and IFN-gamma, alone or in combination, responded to exogenous glutamate (Glu), but not gamma-aminobutyric acid (GABA), indicating that these neurons express glutamate receptors. These NSPC-derived neurons may be promising for neural regenerative strategies in the CNS.
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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 | 0.001 |
| 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".