Environmental oxidant pollutant effects on biologic systems: a focus on micronutrient antioxidant-oxidant interactions
Bibliographic record
Abstract
Oxidative atmospheric pollutants represent a significant source of stress to both terrestrial plants and animals. The biosurfaces of plants and surface-living organisms are directly exposed to these pollutant stresses. These surfaces, including respiratory tract surfaces, contain integrated antioxidant systems that would be expected to provide a primary defense against environmental threats caused by atmospheric reactive oxygen species. When the biosurface antioxidant defenses are overwhelmed, oxidative stress to the cellular components of the exposed biosurfaces can be expected, inducing inflammatory, adaptive, injurious, and reparative processes. Studies of mutants and/or transformed plants and insects, with specific alterations in key components of antioxidant defense systems, offer opportunities to dissect the complex systems that maintain surface defenses against environmental oxidants. In this article, we use a comparative approach to consider interactions of atmospheric oxidant pollutants with selected biosystems, with focus on O3 as the pollutant; plants, flies, skin, and lungs as the exposed biosystems; and nonenzymatic micronutrient antioxidants as significant contributors to overall antioxidant defense strategies of these varied biosystems. Parallelisms among several living organisms, with regard to their protective strategies against environmental atmospheric oxidants, are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.003 |
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".