Impact of a 1.5 T magnetic field on DNA damage in MRI-guided HDR brachytherapy
Bibliographic record
Abstract
PURPOSE: Some studies have suggested that the presence of a static magnetic field (SMF) during irradiation alters biological damage. Since MRI-guided radiotherapy is becoming increasingly common, we constructed a DNA-based detector to assess the effect of a 1.5 T SMF on DNA damage during high dose rate (HDR) brachytherapy irradiation. METHODS: Block phantoms containing a small cavity for the placement of plasmid DNA (pBR322) samples were 3-D printed with biocompatible tissue equivalent material. The phantom was CT scanned and an HDR brachytherapy treatment plan was designed to deliver 20 Gy and 30 Gy doses to the DNA samples in the presence and absence of a 1.5 T SMF. Relative yields of single- and double-strand breaks (SSBs and DSBs, respectively) were computed from gel electrophoresis images of the DNA band intensities and averaged over sample sizes ranging from 12 to 30. Radiation dose was also measured in the presence and absence of the 1.5 T SMF using GafChromic™ EBT3 film placed in the coronal, sagittal, and axial planes. RESULTS: The average yield of DNA with SSBs and DSBs in the presence and absence of the SMF showed no statistically significant differences (all p ≥ 0.17). Differences in the net optical densities of the EBT3 films for each plane were within experimental uncertainty, suggesting no dose difference in the presence and absence of the SMF. CONCLUSIONS: HDR irradiation in the presence of the 1.5 T SMF did not alter dose deposition to the DNA cavity nor change SSB and DSB 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 | 0.001 |
| 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 | 0.000 |
| 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".