Exposure to ambient particulate matter induces oxidative stress in lung and aorta in a size- and time-dependent manner in rats
Bibliographic record
Abstract
Exposure to particulate matter (PM) has been implicated in oxidative stress (OxS) and inflammation as underlying mechanisms of lung damage and cardiovascular alterations. PM is a chemical mixture that can be subdivided according to their aerodynamic size into coarse (CP), fine (FP), and ultrafine (UFP) particulates. We investigated, in a rat model, the induction of OxS (protein oxidation and antioxidant response), carcinogen-DNA adduct formation, and inflammatory mediators in lung in response to different airborne particulate fractions, CP, FP, and UFP, after an acute and subchronic exposure. In addition, OxS was evaluated in the aorta to assess the effects beyond the lungs. Exposure to CP, FP, and UFP induced time- and size-dependent lung protein oxidation and DNA adduct formation. After acute and subchronic exposure, nuclear factor erythroid-2 (Nrf2) activation was observed in the lung, by electrophoretic mobility shift assay, and the induction of mRNA antioxidant enzymes in the FP and UFP groups, but not in the CP. Cytokine concentration of interleukin 1β, interleukin 6, and macrophage inflammatory protein-2 was significantly increased in bronchoalveolar lavage fluid after acute exposure to FP and UFP. Activation of Nrf2 and expression of mRNA antioxidant enzymes were observed only after the subchronic exposure to FP and UFP in the aorta. Our results indicate that FP and UFP were mainly accountable for the oxidant toxic effects in the lung; OxS is spread from the lung to the cardiovascular system. We conclude that the biological mechanisms associated with transient OxS and inflammation are particle size and time-dependent exposure resulting in acute lung injury, which later reaches the vascular system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".