Bulky DNA adduct levels in normal-appearing colon mucosa, and the prevalence of colorectal adenomas
Bibliographic record
Abstract
PURPOSE: Examine the association between bulky DNA adduct levels in colon mucosa and colorectal adenoma prevalence, and explore the correlation between adduct levels in leukocytes and colon tissue. METHODS: P-postlabelling in biopsies of normal-appearing colon tissue and blood donated by 202 patients. Multivariable logistic regression was used to examine associations between DNA adducts, and interactions of DNA adduct-DNA repair polymorphisms, with the prevalence of colorectal adenomas. Correlation between blood and tissue levels of DNA adducts was evaluated using Spearman's correlation coefficient. RESULTS: An interaction between bulky DNA adduct levels and XPA rs1800975 on prevalence of colorectal adenoma was observed. Among individuals with lower DNA repair activity, increased DNA adduct levels were associated with increased colorectal adenoma prevalence (OR = 1.41 per SD increase, 95%CI: 0.92-2.18). Conversely, among individuals with normal DNA activity, an inverse association was observed (OR = 0.60 per SD increase, 95%CI: 0.34-1.07). Blood and colon DNA adduct levels were inversely correlated (ρ = -0.20). CONCLUSIONS: Among genetically susceptible individuals, higher bulky DNA adducts in the colon was associated with the prevalence of colorectal adenomas. The inverse correlation between blood and colon tissue measures demonstrates the importance of quantifying biomarkers in target tissues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".