Monte Carlo-based dosimetry and optimization of a custom alpha cell irradiation setup
Bibliographic record
Abstract
Abstract Objective. When combined with targeting agents, α -particle-emitting radionuclides show promise in treating hypoxic tumors and micrometastases. These radionuclides exhibit a high relative biological effectiveness (RBE), attributed to their high linear energy transfer, and induce complex DNA damage within targeted cells. However, most clinical experience and radiobiological data are derived from photon irradiation. To optimize α -particle-based treatments, further research is needed to refine their RBE estimates. This study aimed to characterize and optimize a custom in-vitro cell irradiation setup for α -particle RBE studies using 241 Am through Monte Carlo simulations. Approach. A Geant4-based Monte Carlo simulation model was used to simulate a custom cell well setup. An 241 Am (48 kBq) source was positioned beneath the well with an adjustable source-to-surface distance (SSD). The spectra of decay products was calculated with 6.5 × 10 9 simulated 241 Am decay events. Simulations were conducted for SSD values of 2 mm, 5 mm, and 7 mm under three scenarios: (A) total dose rate from all decay products, (B) excluding γ -emissions, and (C) excluding secondary particles. Results were compared to published spectra and a published dose rate (0.1 Gy min −1 ) as validation. Main results. The validation dose rate was 0.1136 Gy min −1 . Photons of 13.9–59.5 keV and α -particles of 5.39–5.48 MeV were observed. The dose inhomogeneity across the cells was around 30%, 10%, and 5% in the 2, 5, and 7 mm SSD setups, respectively. The corresponding total dose rates in cells for the three SSDs were 0.583, 0.146, and 0.0830 Gy min −1 . The dose rate contributions were 90% from α -particles, less than 0.07% from γ -emissions, and 9%–10% from secondary particles. Significance. To accurately assess radiobiological effects, it is important to consider the full decay spectrum of radionuclides and their secondary particles in dosimetry calculations. These findings will aid in refining experimental setups for future in-vitro studies, contributing to more reliable RBE calculations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".