Toll-like receptor-4 mediates cigarette smoke-induced cytokine production by human macrophages
Bibliographic record
Abstract
BACKGROUND: The major risk factor for the development of COPD is cigarette smoking. Smoking causes activation of resident cells and the recruitment of inflammatory cells into the lungs, which leads to release of pro-inflammatory cytokines, chemotactic factors, oxygen radicals and proteases. In the present study evidence is found for a new cellular mechanism that refers to a link between smoking and inflammation in lungs. METHODS: Employing human monocyte-derived macrophages, different techniques including FACS analysis, Cytometric Bead Array Assay and ELISA were achieved to evaluate the effects of CS on pro-inflammatory cytokine secretion including IL-8. Then, Toll-like receptor neutralization was performed to study the involvement of Toll-like receptor-4 in IL-8 production. Finally, signaling pathways in macrophages after exposure to CS medium were investigated performing ELISA and Western analysis. RESULTS: We demonstrate that especially human monocytes are sensitive to produce IL-8 upon cigarette smoke stimulation compared to lymphocytes or neutrophils. Moreover, monocyte-derived macrophages produce high amounts of the cytokine. The IL-8 production is dependent on Toll-like receptor 4 stimulation and LPS is not involved. Further research resolved the cellular mechanism by which cigarette smoke induces cytokine production in monocyte-derived macrophages. Cigarette smoke causes subsequently a concentration-dependent phosphorylation of IRAK and degradation of TRAF6. Moreover, IkappaBalpha was phosphorylated which suggests involvement of NF-kappaB. In addition, NFkappaB-inhibitor blocked cigarette smoke-induced IL-8 production. CONCLUSION: These findings link cigarette smoke to inflammation and lead to new insights/therapeutic strategies in the pathogenesis of lung emphysema.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".