The Effect of Nicotine and Tobacco on Aortic Matrix Metalloproteinases in the Production of Aortic Aneurysm
Bibliographic record
Abstract
BACKGROUND: Aortic aneurysms (AAs) are without effective pharmacologic therapy, in clinical usage, in part because of the limited understanding of factors leading to AA development. OBJECTIVE: The objectives of this study were to examine the evidence that cigarette smoking induces AAs through altering matrix metalloproteinases (MMP) and the molecular biology/pharmacology that maybe involved in this effect. METHODS: A systematic search was conducted to identify studies that examined the links between cigarette smoke, MMP and AAs. RESULTS: Eleven studies were identified. There was consistency, between studies. They found that cigarette smoke, nicotine or tobacco products increased aortic dimension and the proportion of AAs. Nicotine and tobacco constituents induced MMPs: MMP-1, MMP-2, MMP-8, MMP-9 and MMP-12 but with different levels of consistency. The molecular mechanisms involved in the pathogenesis of cigarette-induced AA formation, ranked according to the consistency of evidence include JNK, AMPK-α2, Jak Stat, and mTOR/p70Sk and PTEN pathways. CONCLUSION: Nicotine and tobacco constituents translate the exposure to cigarette smoke into increased MMP expression through various molecular mechanisms whose interruption can form the basis for pharmacologic management of AAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".