Short communication Plasma malondialdehyde is induced by smoking: a study with balanced antioxidant profiles
Bibliographic record
Abstract
It has been reported that smokers have higher plasma malondialdehyde concentrations compared with non-smokers. However, smokers have also consistently been shown to have a lower intake of fruits and vegetables as well as lower plasma antioxidant concentrations. Since both the latter issues may well influence the malondialdehyde concentration, we wanted to investigate if the observed difference between smokers and non-smokers was a result of differences in antioxidant status or if a more direct effect of smoking could also be isolated. In the present study, the plasma malondialdehyde and antioxidant profiles of a cohort of smokers (n 48) and non-smokers (n 32) were compared. While there was no significant difference in the major plasma antioxidants measured, i.e. ascorbic acid, a- and g-tocopherol and uric acid, we found a significant effect of smoking on plasma malondialdehyde (P0·0003). Consequently, the present study suggests that lipid peroxidation as measured by plasma malondialdehyde is induced by smoking per se. While poor antioxidant status presumably also affects lipid peroxidation, it is only partly responsible for the increased level found in smokers in general. Malondialdehyde: Antioxidants: Smoking Smoking has long been associated with an increased risk of developing several chronic diseases including atherosclero-sis (McGill, 1990). The process of atherosclerosis is believed to be initiated by lipid peroxidation (Steinberg, 1997).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".