Implications on clinical scenario of gold nanoparticle radiosensitization in regards to photon energy, nanoparticle size, concentration and location
Bibliographic record
Abstract
Gold nanoparticle (AuNP) radiosensitization represents a novel approach to enhance the effectiveness of ionizing radiation. Its efficiency varies widely with photon source energy and AuNP size, concentration, and intracellular localization. In this Monte Carlo study we explored the effects of those parameters to define the optimal clinical use of AuNPs. Photon sources included (103)Pd and (125)I brachytherapy seeds; (169)Yb, (192)Ir high dose rate sources, and external beam sources 300 kVp and 6 MV. AuNP sizes were 1.9, 5, 30, and 100 nm. We observed a 10(3) increase in the rate of photoelectric absorption using (125)I compared to 6 MV. For a (125)I source, to double the dose requires concentrations of 5.33-6.26 mg g(-1) of Au or 7.10 × 10(4) 30 nm AuNPs per tumor cell. For 6 MV, concentrations of 1560-1760 mg g(-1) or 2.17 × 10(7) 30 nm AuNPs per cell are needed, which is not clinically achievable. Examining the proportion of energy transferred to escaping particles or internally absorbed in the nanoparticle suggests two clinical strategies: the first uses photon energies below the k-edge and takes advantage of the extremely localized Auger cascade. It requires small AuNPs conjugated to tumor targeted moieties and nuclear localizing sequences. The second, using photon sources above the k-edge, requires a higher gold concentration in the tumor region. In this approach, energy deposited by photoelectrons is the main contribution to radiosensitization; AuNP size and cellular localization are less relevant.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".