DNA strands break by low energy electrons assessed with Monte Carlo GEANT4 at the atomic level
Bibliographic record
Abstract
Radiotherapy is the mostly used approach for cancer treatment and approximately accounts for $50 \%$ of all cancer therapies. The radiations frequently used are the photons (X-rays, gamma-rays) and others not widely used are the protons and heavy ions. Independently of its energy, any radiation interacts in general by ionizing the medium thus putting electrons in movement. The low energy electrons set in movement and the free radicals and ions are the main products causing damage to DNA. In the present work, we consider the interactions of low energy electrons on the DNA atoms, and by converting the energy deposit into absorbed dose, DNA strand breaks are estimated. The calculations are conducted with Monte Carlo simulation taking advantage of GEANT4 toolkit and by designing around 1000 nucleotides of DNA molecules. The generated electron beams were set to several energies between 2 eV till 500 eV with 10 million simulated electrons. The results show a great similarity with the experimental results for the interactions of excitation, dissociative electron attachment (DEA) and ionization. At 30 eV, ionization in DNA molecule was the dominant interaction type, followed by excitation then by DEA. While at low energy, 4 eV, DEA was found the most dominant, decreasing with increasing energy. The single and double DNA strand breaks were found maximum around 100 eV. In conclusion, such calculations provide more details than experimental results on the types of interactions and their effects on DNA damage, which is of great help specifically in selecting isotopes for internal radiotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".