Role of Resveratrol and Selenium on Oxidative Stress and Expression of Antioxidant and Anti-Aging Genes in Immortalized Lymphocytes from Alzheimer’s Disease Patients
Bibliographic record
Abstract
Oxidative damage is involved in the pathophysiology of age-related ailments, including Alzheimer’s disease (AD). Studies have shown that the brain tissue and also lymphocytes from AD patients present increased oxidative stress compared to healthy controls (HCs). Here, we use lymphoblastoid cell lines (LCLs) from AD patients and HCs to investigate the role of resveratrol (RV) and selenium (Se) in the reduction of reactive oxygen species (ROS) generated after an oxidative injury. We also studied whether these compounds elicited expression changes in genes involved in the antioxidant cell response and other aging-related mechanisms. AD LCLs showed higher ROS levels than those from HCs in response to H2O2 and FeSO4 oxidative insults. RV triggered a protective response against ROS under control and oxidizing conditions, whereas Se exerted antioxidant effects only in AD LCLs under oxidizing conditions. RV increased the expression of genes encoding known antioxidants (catalase, copper chaperone for superoxide dismutase 1, glutathione S-transferase zeta 1) and anti-aging factors (sirtuin 1 and sirtuin 3) in both AD and HC LCLs. Our findings support RV as a candidate for inducing resilience and protection against AD, and reinforce the value of LCLs as a feasible peripheral cell model for understanding the protective mechanisms of nutraceuticals against oxidative stress in aging and AD.
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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".