Mechanisms of Nanoscale Radiation Enhancement by Metal Nanoparticles: Role of Low Energy Electrons
Bibliographic record
Abstract
Metal nanoparticles are considered as highly promising radiosensitizers in cancer radiotherapy. Understanding their radiosensitization mechanisms is critical for future clinical applications. This review is focused on the initial energy deposition by short-range Auger electrons; when high energy radiation is absorbed by gold nanoparticles (GNPs) located near vital biomolecules; such as DNA. Auger electrons and the subsequent production of secondary low energy electrons (LEEs) are responsible for most the ensuing chemical damage near such molecules. We highlight recent progress on DNA damage induced by the LEEs produced abundantly within about 100 nanometers from irradiated GNPs; and by those emitted by high energy electrons and X-rays incident on metal surfaces under differing atmospheric environments. LEEs strongly react within cells; mainly via bound breaking processes due to transient anion formation and dissociative electron attachment. The enhancement of damages induced in plasmid DNA by LEEs; with or without the binding of chemotherapeutic drugs; are explained by the fundamental mechanisms of LEE interactions with simple molecules and specific sites on nucleotides. We address the major challenge of metal nanoparticle and GNP radiosensitization; i.e., to deliver the maximum local dose of radiation to the most sensitive target of cancer cells (i.e., DNA). To achieve this goal the emitted electrons from the absorbed high energy radiation must be short range, and produce a large local density of LEEs, and the initial radiation must have the highest possible absorption coefficient compared to that of soft tissue (e.g., 20-80 keV X-rays).
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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 | 0.001 | 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".