Tumor Necrosis Factor-α Drives 70% of Cigarette Smoke–induced Emphysema in the Mouse
Bibliographic record
Abstract
Mice lacking tumor necrosis factor-alpha (TNF-alpha) receptors (TNFRKO mice) do not develop an inflammatory infiltrate or matrix breakdown after a single acute cigarette smoke exposure. To determine the role of TNF-alpha in the long-term development of emphysema, mice were exposed to smoke for 6 months. TNFRKO mice demonstrated an 11% increase in mean linear intercept; wild-type mice had a 38% increase. TNFRKO mice had 65% fewer neutrophils and no increase in macrophages in lavage fluid. Whole lung matrix metalloprotease (MMP)-2, MMP-9, MMP-12, MMP-13, and matrix type-1 (MT1)-MMP proteins were increased in wild-type mice, but smaller increases in MMP-12, MMP-13, and MT1-MMP were also seen in TNFRKO mice. Lavage matrix breakdown products were elevated in wild-type mice and only partially reduced by anti-neutrophil antibody, implying both neutrophil- and non-neutrophil-mediated matrix breakdown. We conclude that TNF-alpha-mediated processes, probably driving neutrophil influx, are responsible for approximately 70% of airspace enlargement and the majority of inflammatory cell influx/matrix breakdown in the mouse model. TNF-alpha causes increased MMP production, but some increased MMP activity is present even in TNFRKO mice. These findings imply a second TNF-alpha-independent process, possibly related to direct MMP attack on matrix, that produces the remaining 30% of airspace enlargement.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".