p53 Gene Expression in Relation to Indoor Exposure to Unvented Coal Smoke in Xuan Wei, China
Bibliographic record
Abstract
Lung cancer mortality rates in Xuan Wei County, which are among the highest in China, have previously been associated with exposure to indoor emissions from burning smoky coal. To determine if this association is stronger among lung cancer patients with abnormal expression of p53, we performed a population-based case-control study. Ninety-seven newly diagnosed lung cancer patients and 97 controls, individually matched by age, sex, and home fuel type, were enrolled. We used immunocytochemical methods to assess p53 protein accumulation in exfoliated tumor cells isolated from sputum samples. As expected, the amount of lifetime smoky coal use was associated with an overall increase in lung cancer risk. Compared with subjects who used less than 130 tons of smoky coal during their lifetime, the odds ratios (OR) for lung cancer were 1.48 (95% confidence interval [CI], 0.73 to 3.02) for subjects exposed to 130 to 240 tons, and 3.21 (95% CI, 1.23 to 9.03) for subjects who used more than 240 tons of smoky coal (P for trend 0.01). The effect was due almost exclusively to the pattern in women, almost all of whom were nonsmokers. Further, among highly exposed women, the association was substantially larger and achieved statistical significance only among patients with sputum samples that were positive for p53 overexpression (OR, 18.72; 95% CI, 1.77 to 383.38 vs OR, 4.80; 95% CI, 0.66 to 43.87 for p53-negative cases). This study suggests that exposure to the combustion products of smoky coal in Xuan Wei is more strongly associated with women who have lung cancer accompanied by p53 protein overexpression in exfoliated tumor cells.
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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".