MO‐FG‐BRA‐03: A Monte‐Carlo Study of Cellular Dosimetry of Radioactive Gold‐Palladium Nanoparticles
Bibliographic record
Abstract
Purpose: Radioactive gold‐palladium nanoparticles (1⁰3Pd:Pd@Au NPs) are being developed for prostate cancer brachytherapy. Photons emitted by the radioisotope palladium (photon energy: 20.1 and 23.0 keV), interacting with gold‐coating of NPs, lead to enhanced energy distribution in nucleus. Here, a simple cellular model was studied using detailed track‐structure method. Methods: Geant4‐DNA was used with auger electrons enabled. Biological cell was modeled as a sphere of radius r=5 µm that were immersed in a fluid containing large number of NPs at different concentrations (S=1, 2.15, 5.1, 17.2 mg‐Au/g‐H2O). Nucleus was modeled as a concentric sphere (r=3µm). Thickness of gold‐coating on 1⁰3Pd core was 15nm, 20nm and 25nm, respectively. A scenario of NP diffusion was investigated, where S=5.1 mg‐Au/g‐H2O outside cell and S=1 mg‐Au/g‐H2O in cytoplasm. 101⁰ 1⁰3Pd decays were simulated for each combination of NP concentration and gold‐coating. Results: A uniform increase in energy deposition (Edep) is observed in cell nucleus and the energy enhancement ratio (EER) is 1.16, 1.22 and 1.3 for 15nm, 20nm and 25nm of gold ‐coatings, respectively. Edep at the center of nucleus is increased by a factor of 1.47, 2.51 and 5.54 when the NP concentration in the cytoplasm increases from 1 mg‐Au/g‐H2O to 2.15, 5.10 and 17.2 mg‐Au/g‐H2O, respectively. When NPs diffuse into cytoplasm, the mean value of Edep in nucleus increases from 0.42 to 1.13 MeV per 10⁹ decays (GBq‐Second) of 1⁰3Pd and the maximum value increases from 0.54 to 2.5 MeV per GBq‐Second. Conclusion: These results suggest that 1⁰3Pd:Pd@Au NPs constitute a promising nanotherapeutic agent. Ongoing studies use transmission electron microscopy (TEM) images of prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".