White blood cell DNA adducts in a cohort of asthmatic children exposed to environmental tobacco smoke
Bibliographic record
Abstract
PURPOSE: Exposure to environmental tobacco smoke (ETS) leads to molecular damage in the form of DNA adducts. While lung cancer risk is higher among African Americans compared to White Americans, a few studies have tested for racial differences in DNA adducts among children exposed to ETS. The purpose of this study was to test whether African American children have higher DNA adducts levels compared to White children adjusted for ETS exposure. METHODS: Data and biologic specimens were drawn from an existing cohort of 212 asthmatic children. These subjects participated in a 12-month ETS-reduction trial that employed HEPA air cleaners with active filter cartridges and sham filter cartridges. White blood cell (WBC) DNA was analyzed for DNA adducts using (32)P-postlabeling. We assessed ETS exposure using a validated air nicotine dosimeter. We determined the independent relationship between African American race and DNA adduct levels adjusted for ETS exposure and air cleaner use. RESULTS: The mean age of the subjects was 8.4 years; 55% were African American. There was no difference in DNA adduct levels between African American and White children (11.8 vs. 11.2 adducts per 10(9) nucleotides, p = 0.86), despite slightly higher levels of air nicotine exposure (3.4 vs. 2.2 μg/m(3), p = 0.14). African American children used their air cleaners less often than White children. We found that the best predictor of DNA adduct levels was the duration of air cleaner use (r = -0.133, p = 0.056). This association was independent of cartridge type. CONCLUSIONS: We did not see differences in adduct levels by race even after accounting for the level of ETS exposure. However, there was a marginal inverse association between air cleaner use and adducts. Additional research is required to understand this phenomenon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".