Neurotrophic and Neuroprotective Actions of Ginsenosides Rb<sub>1</sub> and Rg<sub>1</sub> <u/>
Bibliographic record
Abstract
The ginsenosides have many pharmacological actions, including various actions on the nervous system. Our previous studies have demonstrated that two ginsenosides, Rb(1) and Rg(1) improve performance in a passive avoidance-learning paradigm and enhance cholinergic metabolism. The present study was designed to examine the cellular neurotrophic and neuroprotective actions of two pure ginsenosides in two model systems. PC12 cells were grown in the absence or presence of nerve growth factor (NGF) as a positive control, and different concentrations of Rb(1) or Rg(1). To assess neurotrophic properties, neurite outgrowth was quantified for representative fields of cells. After 8 days in culture, both ginsenosides enhanced neurite outgrowth in the presence of a sub-optimal dose of (2 ng/ml) NGF, but did not significantly stimulate neurite outgrowth in the absence of NGF. However, after 18 days in culture, both ginsenosides increased neurite outgrowth in the absence of NGF. SN-K-SH cells were grown in the absence or presence of MPTP or beta-amyloid to assess neuroprotection. Rb(1) and Rg(1) both reversed MPTP-induced cell death. beta-Amyloid-induced cell death was not reversed by either ginsenoside, but Rg(1) produced a modest enhancement of cell death in this model. These results suggest that these two ginsenosides have neurotrophic and selective neuroprotective actions that may contribute to the purported enhancement of cognitive function.
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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) | 0.001 | 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) | 0.000 | 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".