A Single Subexcitation‐Energy Electron Can Induce a Double‐Strand Break in DNA Modified by Platinum Chemotherapeutic Drugs
Bibliographic record
Abstract
The sensitization of malignant cells to ionizing radiation is the clinical rationale for the use of platinum-drug-based concurrent chemoradiotherapy (CCRT) for cancer treatment; however, the specific mechanisms of radiosensitization and their respective contributions still remain unknown. Biological mechanisms such as inhibition of DNA repair may contribute to the efficacy of CCRT; nevertheless, there is a dearth of information on the possible contribution of nanoscopic mechanisms to the generation of lethal DNA lesions, such as double-strand breaks (DSB). The present study demonstrates that the abundant near zero-eV (0.5 eV) electrons, created by ionizing radiation during radiotherapy, induce DSB in supercoiled plasmid DNA modified by platinum-containing anticancer drugs (Pt drugs), but not in unmodified DNA. They do so more efficiently than other types of radiation, including soft X-rays and 10 eV electrons. The formation of DSB by 0.5 eV electrons is found to be a single-hit process. These findings reveal insights into the radiosensitization mechanism of Pt drugs that can have implications for the development of optimal clinical protocols for platinum-based CCRT and the deployment of in situ sources of subexcitation-energy electrons (e.g., Auger electron-emitting radionuclides) to efficiently enhance DSB formation in DNA modified by Pt drugs in malignant cells.
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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) | 0.001 | 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".