Hyperthermal Reactions in DNA Triggered by 1–20 eV Electrons: Absolute Cross Sections for Crosslinks, Strand Breaks, Clustered Damages and Base Modifications
Bibliographic record
Abstract
Absolute cross sections (ACSs) are needed to estimate cellular damage induced by high-energy radiation (HER). Low-energy electrons (LEEs), which are the most numerous secondary particles generated by HER, can trigger hyperthermal reactions in DNA. ACSs for such reactions are essential input parameters to calculate radiobiological effectiveness, particularly in targeted radiotherapy. Using a mathematical model, we generate ACSs from effective damage yields induced by LEE impact on 3197 base-pair plasmid DNA films. Direct or enzyme-revealed conformational damages, quantified by electrophoresis, provide the first complete set of ACSs for inducing crosslinks, double-strand breaks (DSBs), single-strand breaks, base-damage-related crosslinks, non-DSB clustered damages (NDCDs), and isolated base damages. These ACSs are generated across the 1–20 eV range, at one eV intervals. They exhibit a strong energy dependence with maximum values at 10 eV of 3.7 ± 0.8, 3.5 ± 0.6, 45.4 ± 4.1, 2.9 ± 1.1, 5.1 ± 1.4, and 54.0 ± 16.4 × 10−15 cm2, respectively. ACSs for DSBs, NDCDs, and crosslinks clearly indicate that lesions threatening cell function and genetic stability can be generated by a single LEE. At 5 and 10 eV, total damage ACSs are 63% and 80% larger, respectively, than those previously determined for the same plasmids bound to arginine, a constituent of histones protecting DNA.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".