Neuroprotective Effects of Ginsenoside Rf on Amyloid-β-Induced Neurotoxicity <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease characterized by the deposition of amyloid-β peptides (Aβ). Aβ accumulation leads to the formation of neurofibrillary tangles, inflammation, axonal injury, synapse loss, and neuronal apoptosis. Thus, reducing Aβ levels should exert a neuroprotective effect against AD. Ginsenoside Rf, an extract from Panax notoginseng, has potent anti-fatigue, anti-nociception, anti-oxidation, and anti-inflammation properties. However, it is unclear whether ginsenoside Rf is effective in the treatment of AD. Here, we reported that ginsenoside Rf could significantly attenuate Aβ-induced apoptosis in N2A cells, as reflected by a dramatic increase in mitochondrial membrane potential and decrease in Ca2 + concentration, reactive oxygen species, and active caspase-3 expression. Meanwhile, ginsenoside Rf could alleviate the Aβ-induced inflammation reaction, such as the decrease of interferon-gamma (IFN-γ) and active caspase-1 expression and the increase of interleukin-13. Furthermore, we also found that Rf is able to accelerate Aβ clearance and subsequently reduces Aβ level in N2A cells stably transfected with human Swedish mutant APP695 (N2A-APP). More importantly, daily Rf treatment (20 mg/kg, i.p.) throughout the experiment dramatically improved spatial learning and memory in Aβ42-induced mouse model of AD. Taken together, these results indicate that ginsenoside Rf may decrease Aβ-induced neurotoxicity and memory decline via anti-inflammatory response during AD development, suggesting that Rf may be a potential therapeutic agent for treating AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".