Tumor Necrosis Factor-α Is Central to Acute Cigarette Smoke–induced Inflammation and Connective Tissue Breakdown
Bibliographic record
Abstract
The role of tumor necrosis factor-alpha (TNF-alpha) as a mediator of cigarette smoke-induced disease is controversial. We exposed mice with knocked-out p55/p75 TNF-alpha receptors (TNF-alpha-RKO mice) to cigarette smoke and compared them with control mice. Two hours after smoke exposure, increases in gene expression of TNF-alpha, neutrophil chemoattractant, macrophage inflammatory protein-2, and macrophage chemoattractant, protein-1 were seen in control mice. By 6 hours, TNF-alpha, macrophage inflammatory protein-2, and macrophage chemoattractant protein-1 gene expression levels had returned to control values in control mice and stayed at control values through 24 hours. In TNF-alpha-RKO mice, no changes in gene expression of these mediators were seen at any time. At 24 hours, control mice demonstrated increases in lavage neutrophils, macrophages, desmosine (a measure of elastin breakdown), and hydroxyproline (a measure of collagen breakdown), whereas TNF-alpha-RKO mice did not. In separate experiments, pure strain 129 mice, which produce low levels of TNF-alpha, showed no inflammatory response to smoke at 24 hours or 7 days. We conclude that TNF-alpha is central to acute smoke-induced inflammation and resulting connective tissue breakdown, the precursor of emphysema. The findings support the idea that TNF-alpha promoter polymorphisms may be of importance in determining who develops smoke-induced chronic obstructive pulmonary disease.
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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".