Radiation dose enhancement using gold nanoparticles with a diamond linear accelerator target: a multiple cell type analysis
Bibliographic record
Abstract
Radiotherapy (RT) is an effective cancer treatment modality, but standard RT often causes collateral damage to nearby healthy tissues. To increase therapeutic ratio, radiosensitization via gold nanoparticles (GNPs) has been shown to be effective. One challenge is that megavoltage beams generated by clinical linear accelerators are poor initiators of the photoelectric effect. Previous computer models predicted that a diamond target beam (DTB) will yield 400% more low-energy photons, increasing the probability of interacting with GNPs to enhance the radiation dose by 7.7-fold in the GNP vicinity. After testing DTB radiation coupled with GNPs in multiple cell types, we demonstrate decreased head-and-neck cancer (HNC) cell viability in vitro and enhanced cell-killing in zebrafish xenografts compared to standard RT. HNC cell lines also displayed increased double-stranded DNA breaks with DTB irradiation in the presence of GNPs. This study presents preclinical responses to GNP-enhanced radiotherapy with the novel DTB, providing the first functional data to support the theoretical evidence for radiosensitization via GNPs in this context, and highlighting the potential of this approach to optimize the efficacy of RT in anatomically difficult-to-treat tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 teacher head, 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".