Fractionated low-dose exposure to ionizing radiation leads to DNA damage, epigenetic dysregulation, and behavioral impairment
Bibliographic record
Abstract
Studies of Fractionated Exposure to Low Doses of Ionizing Radiation (FELDIR) has become of increasing importance to clinical interventions. Its consequences on DNA damage, physical, and mental health have been insufficiently investigated, however. The goal of this study was to determine the effects of FELDIR on the brain using a mouse model. We addressed the levels of DNA damage, global genomic methylation, and DNA methylation machinery in cerebellum, frontal lobe, olfactory bulb and hippocampal tissues, as well as behavioral changes linked to FELDIR exposure. The results reveal increased levels of DNA damage, as reflected by increased occurrence of DNA Strand Breaks (SBs) and dysregulation of stress-response kinase p38. FELDIR also resulted in initial loss of global genomic methylation and altered expression of methyltransferases DNMT1 (down-regulation) and DNMT3a (up-regulation), as well as methyl-binding protein MeCP2 (up-regulation). FELDIR-associated behavioral changes included impaired skilled limb placement on a ladder rung task, increased rearing activity in an open field, and elevated anxiety-like behaviors. The said alterations showed significant dose and tissue specificity. Thus, FELDIR represents a critical impact on DNA integrity and behavioral outcomes that need to be considered in the design of clinical intervention studies.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".