Forsythoside A Mitigates Alzheimer's-like Pathology by Inhibiting Ferroptosis-mediated Neuroinflammation via Nrf2/GPX4 Axis Activation
Bibliographic record
Abstract
Ferroptosis and neuroinflammation play crucial roles in Alzheimer's disease (AD) pathophysiology.Forsythoside A (FA), the main constituent of Forsythia suspensa (Thunb.)Vahl., possesses anti-inflammatory, antibacterial, antioxidant, and neuroprotective properties.The present study aimed to investigate the potential role of FA in AD neuropathology using male APP/PS1 double transgenic AD mice, Aβ1-42-exposed N2a cells, erastin-stimulated HT22 cells, and LPS-induced BV2 cells.FA treatment significantly improved mitochondrial function and inhibited lipid peroxidation in Aβ1-42-exposed N2a cells.In LPS-stimulated BV2 cells, FA treatment decreased the formation of the pro-inflammatory factors IL-6, IL-1β, and NO.In male APP/PS1 mice, FA treatment ameliorated memory and cognitive impairments and suppressed Aβ deposition and p-tau levels in the brain.Analyses using proteomics, immunohistochemistry, ELISA, and western blot revealed that FA treatment significantly augmented dopaminergic signaling, inhibited iron deposition and lipid peroxidation, prevented the activation of IKK/IκB/NF-κB signaling, reduced the secretion of pro-inflammatory factors, and promoted the production of anti-inflammatory factors in the brain.FA treatment exerted anti-ferroptosis and anti-neuroinflammatory effects in erastin-stimulated HT22 cells, and the Nrf2/GPX4 axis played a key role in these effects.Collectively, these results demonstrate the protective effects of FA and highlight its therapeutic potential as a drug component for AD treatment.
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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".