A comparison of oxidative stress in smokers and non-smokers: an in vivo human quantitative study of n-3 lipid peroxidation
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking is believed to cause oxidative stress by several mechanisms, including direct damage by radical species and the inflammatory response induced by smoking, and would therefore be expected to cause increased lipid peroxidation. The aim was to carry out the first study of the relationship of smoking in humans to the level of n-3 lipid peroxidation indexed by the level of ethane in exhaled breath. METHODS: Samples of alveolar air were obtained from 11 smokers and 18 non-smokers. The air samples were analyzed for ethane using mass spectrometry. RESULTS: The two groups of subjects were matched with respect to age and gender. The mean cumulative smoking status of the smokers was 11.8 (standard error 2.5) pack-years. The mean level of ethane in the alveolar breath of the group of smokers (2.53 (0.55) ppb) was not significantly different from that of the group of non-smokers (2.59 (0.29) ppb; p = 0.92). With all 29 subjects included, the Spearman rank correlation coefficient between ethane levels and cumulative smoking status was -0.11 (p = 0.58), while an analysis including only the smokers yielded a corresponding correlation coefficient of 0.11 (p = 0.75). CONCLUSION: Our results show no evidence that cigarette smoking is related to increased n-3 lipid peroxidation as measured by expired ethane.
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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 | 0.000 |
| 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".