Resveratrol Mitigates Oxygen and Glucose Deprivation-Induced Inflammation, NLRP3 Inflammasome, and Oxidative Stress in 3D Neuronal Culture
Bibliographic record
Abstract
Oxygen glucose deprivation (OGD) can produce hypoxia-induced neurotoxicity and is a mature in vitro model of hypoxic cell damage. Activated AMP-activated protein kinase (AMPK) regulates a downstream pathway that substantially increases bioenergy production, which may be a key player in physiological energy and has also been shown to play a role in regulating neuroprotective processes. Resveratrol is an effective activator of AMPK, indicating that it may have therapeutic potential as a neuroprotective agent. However, the mechanism by which resveratrol achieves these beneficial effects in SH-SY5Y cells exposed to OGD-induced inflammation and oxidative stress in a 3D gelatin scaffold remains unclear. Therefore, in the present study, we investigated the effect of resveratrol in 3D gelatin scaffold cells to understand its neuroprotective effects on NF-κB signaling, NLRP3 inflammasome, and oxidative stress under OGD conditions. Here, we show that resveratrol improves the expression levels of cell viability, inflammatory cytokines (TNF-α, IL-1β, and IL-18), NF-κB signaling, and NLRP3 inflammasome, that OGD increases. In addition, resveratrol rescued oxidative stress, nuclear factor-erythroid 2 related factor 2 (Nrf2), and Nrf2 downstream antioxidant target genes (e.g., SOD, Gpx GSH, catalase, and HO-1). Treatment with resveratrol can significantly normalize OGD-induced changes in SH-SY5Y cell inflammation, oxidative stress, and oxidative defense gene expression; however, these resveratrol protective effects are affected by AMPK antagonists (Compounds C) blocking. These findings improve our understanding of the mechanism of the AMPK-dependent protective effect of resveratrol under 3D OGD-induced inflammation and oxidative stress-mediated cerebral ischemic stroke conditions.
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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) | 0.001 | 0.001 |
| 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".