Resveratrol and quercetin, two natural polyphenols, reduce apoptotic neuronal cell death induced by neuroinflammation
Bibliographic record
Abstract
Parkinson's disease (PD) is a movement disorder characterized by a progressive loss of nigrostriatal dopaminergic neurons. Microglia activation and neuroinflammation have been associated with the pathogenesis of PD. Indeed, cytokines have been proposed as candidates that mediate the apoptotic cell death of dopaminergic neurons seen in PD. In this study, we investigated the effect of two natural polyphenols, resveratrol and quercetin, on neuroinflammation. For glial cells, we observed that lipopolysaccharide (LPS)-induced mRNA levels of two proinflammatory genes, interleukin 1-alpha and tumor necrosis factor-alpha, are strongly decreased by treatments with resveratrol or quercetin. We also undertook microglial-neuronal coculture to examine the influence of resveratrol and quercetin on dopaminergic neuronal cell death evoked by LPS-activated microglia. Cytotoxicity assays were performed to evaluate the percentage of cell death, with apoptotic cells identified by both the TdT-mediated dUTP nick end labeling technique and the detection of cleaved caspase-3. We report that treatment of N9 microglial cells with resveratrol or quercetin successfully reduced the inflammation-mediated apoptotic death of neuronal cells in our coculture system. Altogether our results demonstrate that resveratrol and quercetin diminished apoptotic neuronal cell death induced by microglial activation and suggest that these two phytoestrogens may be potent antiinflammatory compounds.
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) | 0.000 | 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".