DNA-Reactive Carcinogens: Mode of Action and Human Cancer Hazard
Bibliographic record
Abstract
It has been known for decades that mutagenicity plays an important role in the activity of most carcinogens. This mutagenicity can result from direct damage to DNA through a chemical being DNA reactive or from indirect effects, such as through the production of oxygen radicals that then react with DNA. This article presents a set of key events whereby DNA reactivity initiates the process of carcinogenicity that leads to the subsequent mutation induction and enhanced cell proliferation that ultimately results in tumor development. This set of key events for DNA-reactive chemicals was applied to two case studies (aflatoxin B1 and dichloromethane) with the aim of assessing the utility of the Human Relevance Framework (HRF) for this class of chemicals. The conclusions were that the HRF was a viable approach for the use of mechanistic data for DNA-reactive chemicals obtained from both laboratory animals and human cells in vivo and in vitro for predicting human carcinogenicity. In the case of aflatoxin B1, the HRF could be used to predict that carcinogenicity in humans was a likely outcome. In contrast, the HRF predicted that the human carcinogenic potential of dichloromethane was at best less likely than in rodents; this conclusion was supported by the available epidemiological data.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".