The Role of Polyphenols on Blue Light‐Induced Retinal Pigment Cell Damage
Bibliographic record
Abstract
The increased exposure of eyes to blue light is a growing concern in our everyday lives. Blue light is responsible for cell damage, cell death, and oxidative stress all of which can lead to vision loss. Resveratrol and pterostilbene are polyphenols found in the skin of various fruits including grapes and blueberries and have been known for antioxidant properties. The aim of this study was to investigate the protective role of resveratrol and pterostilbene on cell death, cell proliferation, and oxidative stress in Human Retinal Pigment Epithelial (ARPE‐19) cells exposed to blue light. ARPE‐19 cells were pre‐incubated with either; 50 or 100uM of resveratrol or 10 or 50uM of pterostilbene for 4 hours followed by exposure to blue light (475nm) for 12 hours. Subsequent to the blue light exposure, cellular viability, oxidative stress, and death were measured in each treatment condition. Blue light exposure resulted in a decrease in cell viability, an increase in caspase 3/7 activation, and cell death. This was accompanied by an increase in protein expression of catalase, and manganese superoxide dismutase (MnSOD) and an increase in 4‐Hydroxynoneal (4HNE) protein adduct formation. In conclusion, resveratrol mitigated the damaging effects of blue light on ARPE‐19 cells by attenuating cell death and oxidative stress. This in‐vitro study is the first step into understanding how polyphenols can possibly be used to reduce the oxidative damage caused by blue light in the eyes and prevent unnecessary vision loss.
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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".