Lipid Peroxidation as the Mechanism Underlying Polycyclic Aromatic Hydrocarbons and Sunlight Synergistic Toxicity in Dermal Fibroblasts
Bibliographic record
Abstract
Light and atmospheric pollution are both independently implicated in cancer induction and premature aging. Evidence has been growing more recently on the toxic synergy between light and pollutants. Polycyclic aromatic hydrocarbons (PAHs) originate from the incomplete combustion of organic matter. Some PAHs, such as the Benzo[a]pyrene (BaP), absorb ultraviolet A (UVA) wavelengths and can act as exogenous chromophores, leading to synergistic toxicity through DNA damage and cytotoxicity concomitant to ROS formation. In this study, we shed light on the mechanism underlying the toxic synergy between PAHs and UVA. Using dermal fibroblasts co-exposed to UVA and BaP, we have demonstrated that the photosensitization reaction causes mortality, which is most likely caused by ROS accumulation. We have shown that these ROS are concentrated in the lipids, which causes an important induction of lipid peroxidation and malondialdehyde, by-products of lipid peroxidation. We have also shown the accumulation of bulky DNA damage, most likely generated by these by-products of lipid peroxidation. To our knowledge, this study represents the first one depicting the molecular effects of photo-pollution on dermal skin.
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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".