Effects of Formaldehyde on DNA: A Molecular Dynamics Study of Formaldehyde Adducts
Bibliographic record
Abstract
Formaldehyde is a known carcinogen, which damages and mutates DNA in the cells of mammals. Acute and chronic exposure to formaldehyde may occur in both occupational settings (e.g., embalmers) and from several household products (e.g., insulation, particle boards, and carpeting). Upon human exposure to formaldehyde, several DNA adducts may be formed, including those with the amino groups of guanine (denoted CH2OH-N2-G), cytosine (CH2OH-N4-C), and adenine (CH2OH-N6-A). Each of these adducts consists of a methoxy moiety linked to the exocyclic amino group of the respective DNA nucleobase. The methoxy moiety can interfere with several noncovalent interactions within a DNA helix, including Watson-Crick hydrogen bonding, Hoogsteen hydrogen bonding, and stacking interactions between neighboring DNA nucleobases. These interactions are essential to the normal function of DNA and their disruption could lead to mutations when DNA is copied, which in turn lead to health effects such as cancer. Although insight into the implications on formaldehyde adducts are currently lacking from experimental studies, computational chemistry can be used to predict the effects of the methoxy moiety on the nucleobase interactions and the mutagenicity of these adducts. In the current study, molecular dynamics (MD) simulations, along with advanced structural analysis and energy calculations, were used to study the preferred conformations of the nucleobase within damaged DNA, and thereby characterize the structural impacts of this damage. Using this analysis, key insight was gained into the mutagenicity of the CH2OH adducts and the data obtained can be used to direct future biochemical studies of the harmful effects of these lesions. *Indicates presenter
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".