90-Day Repeated Inhalation Exposure of Surfactant Protein-C/Tumor Necrosis Factor- <i>α</i> (SP-C/TNF- <i>α</i> ) Transgenic Mice to Air Pollutants
Bibliographic record
Abstract
Tumor necrosis factor (TNF)-alpha, a cytokine present in inflammed lungs, is known to mediate some of the adverse effects of ozone and inhaled particles. The authors evaluated transgenic mice with constitutive pulmonary expression of TNF-alpha under transcriptional regulation of the surfactant protein-C promoter as an animal model of biological susceptibility to air pollutants. To simulate a repeated, episodic exposure to air pollutants, wild-type and TNF mice inhaled air or a mixture of ozone (0.4 ppm) and urban particles (EHC-93, 4.8 mg/m3) for 4 h, once per week, for 12 consecutive weeks and were sacrificed 20 h after last exposure. TNF mice exhibited chronic lung inflammation with septal thickening, alveolar enlargement, and elevated protein and cellularity in bronchoalveolar lavage fluid (genotype main effect, p < .001). Repeated exposure to pollutants did not result in measurable inflammatory changes in wild-type mice and did not exacerbate the inflammation in TNF mice. The pollutants decreased recovery of alveolar macrophages in tavage fluid of both wild-type and TNF mice (exposure main effect, p < .001). Exacerbation of the rate of protein nitration reactions specifically in the lungs of TNF mice was revealed by the high ratio of 3-nitrotyrosine to L-DOPA after exposure to the air pollutants (Genotype x Exposure factor interaction, p = .014). Serum creatine kinase-MM isoform increased in TNF mice exposed to pollutants (Genotype X Exposure factor interaction, p = .043). The marked pollutant-related nitration in the lungs of the TNF mice reveals basic differences in free radical generation and scavenging in the inflamed lungs in response to pollutants. Furthermore, elevation of circulating creatine kinase-MM isoform specifically in TNF mice exposed to pollutants suggests systemic adverse impacts from lung inflammatory mediators, possibly on muscles and the cardiovascular 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".