Aryl hydrocarbon receptor attenuation of sub-chronic cigarette smoke-induced pulmonary neutrophilia is associated with retention of nuclear RelB and suppression of intercellular adhesion molecule-1 (CAM1P.233)
Bibliographic record
Abstract
Abstract Cigarette smoke exposure is associated with chronic and enhanced pulmonary inflammation characterized by increased cytokine production and leukocyte recruitment to the lung. Although the aryl hydrocarbon receptor (AhR) is well-known to mediate toxic effects of environmental contaminants, the AhR has emerged as a suppressor of acute cigarette smoke-induced neutrophilia. As there is currently no information on the AhR prevention of lung inflammation due to varied and prolonged exposure regimes, we exposed control and AhR-/- mice to cigarette smoke for 2 weeks (sub-chronic exposure) utilizing low and high exposure protocols and evaluated pulmonary inflammation. Sub-chronic cigarette smoke exposure increased pulmonary neutrophilia dose-dependently in AhR-/- mice. There was no difference between smoke-exposed AhR+/- and AhR-/- mice in the expression of cytokines associated with neutrophil recruitment. However, expression of pulmonary intercellular adhesion molecule-1 (ICAM-1), an adhesion molecule involved in neutrophil migration and retention, was higher in AhR-/- mice. Nuclear RelB, an anti-inflammatory NFkB transcription factor, was significantly lower in sub-chronically exposed AhR-/- mice. These data support that absence of the AhR contributes to heightened pulmonary neutrophilia in response to on-going cigarette smoke exposure. Inter-individual variations in AhR expression may enhance the susceptibility to cigarette smoke induced diseases.
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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 | 0.001 |
| 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".