Oxidative DNA damage and repair in teratogenesis and neurodevelopmental deficits
Bibliographic record
Abstract
Several teratogenic agents, including ionizing radiation and xenobiotics such as phenytoin, benzo[a]pyrene, thalidomide, and methamphetamine, can initiate the formation of reactive oxygen species (ROS) that oxidatively damage cellular macromolecules including DNA. Oxidative DNA damage, and particularly the most prevalent 8-oxoguanine lesion, may adversely affect development, likely via alterations in gene transcription rather than via a mutational mechanism. Contributions from oxidative DNA damage do not exclude roles for alternative mechanisms of initiation like receptor-mediated processes or the formation of covalent xenobiotic-macromolecular adducts, damage to other macromolecular targets like proteins and lipids, and other effects of ROS like altered signal transduction. Even in the absence of teratogen exposure, endogenous developmental oxidative stress can have embryopathic consequences in the absence of key pathways for detoxifying ROS or repairing DNA damage. Critical proteins in pathways for DNA damage detection/repair signaling, like p53 and ataxia telangiectasia mutated, and DNA repair itself, like oxoguanine glycosylase 1 and Cockayne syndrome B, can often, but not always, protect the embryo from ROS-initiating teratogens. Protection may be variably dependent upon such factors as the nature of the teratogen and its concentration within the embryo, the stage of development, the species, strain, gender, target tissue and cell type, among other factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".