LATE-BREAKING ABSTRACT: Inhaled diesel exhaust alters immune response proteins in the bronchial secretome in humans
Bibliographic record
Abstract
Rationale: Diesel exhaust (DE), a paradigm for air pollution, is associated with respiratory diseases. Protein changes following DE exposure in humans are poorly elucidated. Objective: To define changes in the bronchial secretome following exposure to inhaled DE, using a controlled human exposure study. Methods: Mild asthmatics inhaled filtered air (FA) and DE (300 mg/m3) for 2h (crossover; random order). Bronchoalveolar lavage (BAL) was obtained 48 hr after each exposure. Pooled BAL (n=5 per condition) was processed by LC-MS/MS. Protein expression intensity was determined by spectral counting. Results: Expressions of 340 secreted proteins were significantly altered in response to inhaled DE compared to FA. Expressions of 38 proteins were enhanced >4-fold in response to DE compared to FA (Fig. 1). DE-induced proteins were overrepresented in CDK5, Cdc42 and clathrin-mediated endocytosis pathways. Upstream regulators predicted to be activated were IFNA2 and retinoic acid. DE enhanced proteins related to immune processes (Fig. 1). Conclusion: This is the first comprehensive interrogation of the airway secretome following inhaled DE exposure in humans. This study details DE-driven induction of proteins related to immune responses in the bronchial environment. Fig. 1 Proteins altered by inhaled DE, interactome by Ingenuity Pathway Analysis: Red = induced by DE; Green = suppressed by DE.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".