Concentrations of Blood Vitamin A, C, E, Coenzyme Q10 and Urine Cotinine Related to Cigarette Smoking Exposure
Bibliographic record
Abstract
BACKGROUND: In smokers, smoking causes many disease entities including cancers, chronic pulmonary diseases and cardiovascular diseases. Passive smoking is also accepted as a carcinogen and its adverse health effects are emphasized. We measured blood vitamin A, C, E (alpha-, beta- and gamma-tocopherol), coenzyme Q10 and urine cotinine concentrations in nonsmokers and smokers. METHODS: Twenty-one healthy nonsmokers and 24 healthy smokers were included in this study. Smoking status was assessed with a self-reported questionnaire. Plasma was analyzed for coenzyme Q10 and serum for vitamin A, C, E using HPLC (Agilent Technologies Inc., USA) and random urine for cotinine using LC/tandem mass spectrometry (Applied Biosystems Inc., Canada). RESULTS: Smokers had significantly lower serum concentrations of vitamin C than nonsmokers (P=0.0005). No significant differences in concentrations of serum vitamin A, E, and plasma coenzyme Q10 were observed. Smokers had highly elevated urine cotinine levels (1,454+/-903 ng/mL). In 16 (76.2%) of 21 nonsmokers, urine cotinine was detected (3.25+/-4.08 ng/mL). The correlations between urine cotinine and blood antioxidants levels were not found. Neither, the correlation between smoking status and blood antioxidants & urine cotinine was found. CONCLUSIONS: This study shows that smokers had significantly lower vitamin C levels among nonenzymatic antioxidants, namely, vitamin A, C, E and coenzyme Q10. High detection rate of urine cotinine in nonsmokers show the seriousness of passive smoking exposure, therefore more social efforts should be directed to reduce passive smoking exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".