Contribution of IL-1β and TNF-α to the initiation of the peripheral lung response to atmospheric particulates (PM<sub>10</sub>)
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".