Effect of ginsenoside Rg1 on proliferation and neural phenotype differentiation of human adipose-derived stem cells in vitro
Bibliographic record
Abstract
AIMS: To investigate whether ginsenoside Rg1 can promote neural phenotype differentiation of human adipose-derived stem cells (hASCs) in vitro. METHODS: hASCs were isolated from lipo-aspirates, and characterized by specific cell markers and multilineage differentiation capacity after culturing to the 3rd passage. Cultured hASCs were treated with neural inductive media alone (group A, control) or inductive media plus 10, 50, or 100 μg/mL ginsenoside Rg1 (groups B, C, and D, respectively). Cell proliferation was assessed by CCK-8 assay. Neuron specific enolase (NSE) and microtubule-associated protein-2 (MAP-2) levels were measured by Western blot. mRNA levels of growth associated protein-43 (GAP-43), neural cell adhesion molecule (NCAM), and synapsin-1 (SYN-1) were determined by real-time PCR. RESULTS: Ginsenoside Rg1 promoted the proliferation of hASCs (groups B, C, and D) and resulted in higher expression of NSE and MAP-2 compared with the control group. Gene expression levels of GAP-43, NCAM, and SYN-1 in the test groups were higher than that in thw control. The results displayed a dose-dependent effect of ginsenoside Rg1 on cell proliferation and neural phenotype differentiation. CONCLUSION: This study indicated that ginsenoside Rg1 promotes cell proliferation and neural phenotype differentiation of hASCs in vitro, suggesting a potential use for hASCs in neural regeneration medicine.
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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".