Impact of emerging pollutants on pulmonary inflammation in asthmatic rats: ethanol vapors and agglomerated TiO<sub>2</sub>nanoparticles
Bibliographic record
Abstract
CONTEXT: Titanium dioxide nanoparticles (nano-TiO(2)) and ethanol vapors are air contaminants with increasing importance. The presence of a pathological pulmonary condition, such as asthma, may increase lung susceptibility to such contaminants. OBJECTIVE: This study aimed to investigate if exposure to inhaled ethanol vapors or nano-TiO(2) can modulate the rat pulmonary inflammatory response resulting from an allergic asthmatic reaction. MATERIALS AND METHODS: Brown Norway rats were sensitized (sc) and challenged (15 min inhalation, 14 days later) with chicken egg ovalbumin (OVA). Leukocytes were counted in bronchoalveolar lavages (BAL) performed at 6, 24, 36, 48 and 72 h following the challenge and either after ethanol exposures (3000 ppm, 6 h/day, daily) or at 48 h (peak inflammation) for nano-TiO(2) exposures (9.35 mg/m(3) aerosol for 6 and 42 h after the OVA challenge). For the nano-TiO(2) exposures, plasma and BAL cytokines were measured and lung histological analyzes were performed. RESULTS: Exposure to ethanol did not significantly affect BAL leukocytes after OVA challenge. Exposure to nano-TiO(2) significantly decreased BAL leukocytes compared to OVA-challenged controls. Plasma and BAL IL-4, IL-6, and INF-γ levels were also decreased in the nano-TiO(2) group. DISCUSSION: While ethanol vapors do not modify the pulmonary inflammation in rats during an asthmatic response, a surprising protective effect for agglomerated nano-TiO(2) was observed. A putative mechanistic basis involving a decrease in the Th2 response caused by OVA is proposed. CONCLUSION: Allergic pulmonary inflammation is not up-regulated by inhalation of the pollutants ethanol and nano-TiO(2). On the contrary, nano-TiO(2) decreases lung inflammation in asthmatic rats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".