Cigarette smoke extract profoundly suppresses TNFα-mediated proinflammatory gene expression through upregulation of ATF3 in human coronary artery endothelial cells
Bibliographic record
Abstract
Endothelial dysfunction caused by the combined action of disturbed flow, inflammatory mediators and oxidants derived from cigarette smoke is known to promote coronary atherosclerosis and increase the likelihood of myocardial infarctions and strokes. Conversely, laminar flow protects against endothelial dysfunction, at least in the initial phases of atherogenesis. We studied the effects of TNFα and cigarette smoke extract on human coronary artery endothelial cells under oscillatory, normal laminar and elevated laminar shear stress for a period of 72 hours. We found, firstly, that laminar flow fails to overcome the inflammatory effects of TNFα under these conditions but that cigarette smoke induces an anti-oxidant response that appears to reduce endothelial inflammation. Elevated laminar flow, TNFα and cigarette smoke extract synergise to induce expression of the transcriptional regulator activating transcription factor 3 (ATF3), which we show by adenovirus driven overexpression, decreases inflammatory gene expression independently of activation of nuclear factor-κB. Our results illustrate the importance of studying endothelial dysfunction in vitro over prolonged periods. They also identify ATF3 as an important protective factor against endothelial dysfunction. Modulation of ATF3 expression may represent a novel approach to modulate proinflammatory gene expression and open new therapeutic avenues to treat proinflammatory 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 | 0.000 |
| 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".