Indenopyrene and Blue-Light Co-Exposure Impairs the Tightly Controlled Activation of Xenobiotic Metabolism in Retinal Pigment Epithelial Cells: A Mechanism for Synergistic Toxicity
Bibliographic record
Abstract
High energy visible (HEV) blue light is an increasing source of concern for visual health. Polycyclic aromatic hydrocarbons (PAH), a group of compounds found in high concentrations in smokers and polluted environments, accumulate in the retinal pigment epithelium (RPE). HEV absorption by indeno [1,2,3-cd]pyrene (IcdP), a common PAH, synergizes their toxicities and promotes degenerative changes in RPE cells comparable to the ones observed in age-related macular degeneration. In this study, we decipher the processes underlying IcdP and HEV synergic toxicity in human RPE cells. We found that IcdP-HEV toxicity is caused by the loss of the tight coupling between the two metabolic phases ensuring IcdP efficient detoxification. Indeed, IcdP/HEV co-exposure induces an overactivation of key actors in phase I metabolism. IcdP/HEV interaction is also associated with a downregulation of proteins involved in phase II. Our data thus indicate that phase II is hindered in response to co-exposure and that it is insufficient to sustain the enhanced phase I induction. This is reflected by an accelerated production of endogenous reactive oxygen species (ROS) and an increased accumulation of IcdP-related bulky DNA damage. Our work raises the prospect that lifestyle and environmental pollution may be significant modulators of HEV toxicity in the retina.
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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".