The Acute Effect of Multi-Ingredient Antioxidant Supplementation following Ionizing Radiation
Bibliographic record
Abstract
Radiation exposure is an undeniable health threat encountered in various occupations and procedures. High energy waves in ionizing radiation cause DNA damage and induce reactive oxygen species (ROS) production, which further exacerbate DNA, protein, and lipid damage, increasing risk of mutations. Although endogenous antioxidants such as superoxide dismutase have evolved to upregulate and neutralize ROS, exogenous dietary antioxidants also have the potential to combat ionizing radiation (IR)-induced ROS production. We evaluated a cocktail of ingredients (AOX) purported to have antioxidant and mitochondrial protective properties on the acute effects of IR. We show that IR stimulates DNA damage through phosphorylation of DNA repair proteins in the heart, brain, and liver of mice. AOX showed partial protection in brain and liver, through a lack of significant activation in given repair proteins. In addition, AOX attenuated the IR-induced increase in NF-kβ mRNA and protein expression in brain and liver. Lastly, cytochrome c oxidase complex transcripts were significantly higher in heart and brain following radiation, which was also diminished by prior ingestion of AOX. Together, our findings suggest that a multi-ingredient AOX supplement may attenuate the IR-induced cellular damage response and represents a feasible and cost-effective preventative supplement for at-risk populations of radiation exposure.
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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 | 0.000 | 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) | 0.001 | 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".