Contribution of IL-1β and TNF-α to the initiation of the peripheral lung response to atmospheric particulates (PM<sub>10</sub>)
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Bibliographic record
Abstract
Alveolar macrophages (AM) play a key role in clearing atmospheric particulates from the lung surface and stimulating epithelial cells to produce proinflammatory mediators. The present study examines the role of "acute response" cytokines TNF-alpha and IL-1 beta released by AM exposed to ambient particulate matter with a diameter of <10 microm (PM(10)) in amplifying the proinflammatory mediator expression by A549 cells and human bronchial epithelial cells (HBEC). The results showed that supernatants from human AM incubated 24 h with PM(10) (100 microg/ml) contained more TNF-alpha, IL-1 beta, granulocyte-macrophage colony stimulating factor, IL-6, and IL-8 than nonexposed AM supernatants. The 3-h treatment of A549 cells with PM(10)-exposed AM supernatants increased TNF-alpha, IL-1 beta, IL-8, regulated on activation normal T-cells expressed and secreted (RANTES), and leukemia inhibitory factor mRNA compared with the treatment with nonexposed AM supernatants and, compared with untreated A549 cells, additionally increased ICAM-1 and monocyte chemotactic protein-1 mRNA. Preincubating PM(10)-exposed AM supernatants with anti-IL-1 beta antibodies reduced all the above mediators as well as VEGF mRNA expression (P < 0.05), while anti-TNF-alpha antibodies were less effective (P > 0.05), and the combination of the two antibodies most effective. When HBEC were treated similarly, anti-TNF-alpha antibodies had the greatest effect. In A549 cells PM(10)-exposed AM supernatants increased NF-kappa B, activator protein (AP)-1 and specificity protein 1 binding, while anti-TNF-alpha and anti-IL-1 beta antibodies reduced NF-kappa B and AP-1 binding. We conclude that AM-derived TNF-alpha and IL-1 beta provide a major stimulus for the production of proinflammatory mediators by lung epithelial cells and that their relative importance may depend on the type of epithelial cell target.
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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 | 0.001 |
| 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 it