Adaptive Response with Oxidative Stress from CT Scans and Exercise in Mice
Bibliographic record
Abstract
We speculated that oxidative stress produced by repeated low dose diagnostic CT scans (10 mGy/scan, 2d/wk, 10 wk) would induce an adaptive response (AR) in C57BL/6 mice. We postulated that additional oxidative stress produced by exercise (1hr, 3d/wk, 10wk could further enhance the AR. We analyzed DNA double strand break (DSB) levels and genomic damage to stem cells via gH2AX foci and micronucleated reticulocytes (MN‐RET) flow cytometric assays, respectively. Following a challenge dose (1 Gy) in vitro, cells from CT mice had a significant increase (14%) in DNA DSBs compared to control and CT/exercised mice (p<0.035). A higher 2 Gy challenge dose showed that there was a significant reduction (16%) in DNA DSBs in CT/exercised mice (p = 0.002). When genomic damage was assessed, mice that had CT scans had significantly higher levels of MN‐RET (46% higher) compared to controls (p < 0.009). Interesting, the CT/exercise mice did not have any detectable increase in MN‐RET levels. After an in vivo 2 Gy challenge dose, control mice and CT mice had similar high levels of induced MN‐RET but there was a significant reduction (14%) in MN‐RET levels in the CT/exercise mice (p< 0.025). This research shows that CT scans can induce an AR in mice; however, this response is significantly modified by exercise. This research was supported by US DOE, Low Dose Research Program (DE‐FG02‐07ER64343) and NSERC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".