Exposure to Long Magnetic Resonance Imaging Thermometry Does Not Cause Significant DNA Double-Strand Breaks on CF-1 Mice
Bibliographic record
Abstract
The purpose of the study was to investigate if the high gradient strength and slew rate used for long MRI-thermometry monitoring could cause DNA double-stranded breaks (DSBs). To this end, an enzyme-linked immunosorbent assay (ELISA) was used to quantify γH2AX, a molecular marker for DSBs, in the blood of mice after a 6-hour exposure to magnetic resonance imaging (MRI). Fourteen CF-1 female mice were separated into 4 experimental groups: Untreated negative control, MRI-treated, MRI-Control, and exposed to ionizing radiation positive control. Untreated negative control was used as a baseline for ELISA to quantify γH2AX. MRI-treated consisted of a 6-hour continuous magnetic resonance imaging (MRI) echo planar imaging (EPI) sequence with a slew rate of 192 mT/m/s constituting a significantly longer imaging time than routine clinical imaging. MRI-control mice were maintained under the same conditions outside the MRI scanner for 6-hours. Mice in the irradiation group served as a positive control of DSBs and were exposed to either 2 Gy, 5 Gy or 10 Gy of ionizing radiation. DSBs in the blood lymphocytes from the treatment groups were analyzed using the γH2AX ELISA and compared. Total protein concentration in lysates was determined for each blood sample and averaged 1 ± 0.35 mg/mL. Irradiated positive controls were used to test radiation dose-dependency of the γH2AX ELISA assay where a linear dependency on radiation exposure was observed (r2 = 0.93) between untreated and irradiated samples. Mean and standard error mean of γH2AX formation were calculated and compared between each treatment group. Repeated measures 1-way ANOVA showed statistically significant differences between the means of irradiated controls and both the MRI-control and MRI-treated groups. There was no statistically significant difference between the MRI-treated samples and the MRI-control groups. Our results show that long MRI exposure at a high slew rate did not cause increased levels of γH2AX when compared to control mice, suggesting that no increase in DSBs was caused by the long MR thermometry imaging session. The novelty of this work contradicts other studies that have suggested MRI may cause DSBs; this work suggests an alternative cause of DNA damage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".