Gold Nanoparticles in Radiotherapy and Recent Progress in Nanobrachytherapy
Bibliographic record
Abstract
Over the last few decades, gold nanoparticles (GNPs) have emerged as "radiosensitizers" in oncology. Radiosensitizers are additives that can enhance the effects of radiation on biological tissues treated with radiotherapy. The interaction of photons with GNPs leads to the emission of low-energy and short-range secondary electrons, which in turn increase the dose deposited in tissues. In this context, GNPs are the subject of intensive theoretical and experimental studies aiming at optimizing the parameters leading to greater dose enhancement and highest therapeutic effect. This review describes the main mechanisms occurring between photons and GNPs that lead to dose enhancement. The outcome of theoretical simulations of the interactions between GNPs and photons is presented. Finally, the findings of the most recent in vivo studies about interactions between GNPs and photon sources (e.g., external beams, brachytherapy sources, and molecules labeled with radioisotopes) are described. The advantages and challenges inherent to each of these approaches are discussed. Future directions, providing new guidelines for the successful translation of GNPs into clinical applications, are also highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".