Nanodosimetry of Auger electrons: A case study from the decay of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mrow/><mml:mn>125</mml:mn></mml:msup></mml:math>I and 0–18-eV electron stopping cross sections of cytosine
Bibliographic record
Abstract
Radiopharmaceuticals emitting Auger electrons are often injected into patients undergoing cancer treatment with targeted radionuclide therapy (TRT). In this type of radiotherapy, the radiation source is radial and most of the emitted primary particles are low-energy electrons (LEEs) having kinetic energies distributed mostly from zero to a few hundred electron volts with very short ranges in biological media. These LEEs generate a high density of energy deposits and clustered damage, thus offering a relative biological effectiveness comparable to that of alpha particles. In this paper, we present a simple model and corresponding measurements to assess the energy deposited near the site of the radiopharmaceuticals in TRT. As an example, a calculation is performed for the decay of a single ${}^{125}$I radionuclide surrounded by a 1-nm-radius spherical shell of cytosine molecules using the energy spectrum of LEEs emitted by ${}^{125}$I along with their stopping cross sections between 0 and 18 eV. The dose absorbed by the cytosine shell, which occupies a volume of 4 nm${}^{3}$, is extremely high. It amounts to 79 kGy per decay of which 3$%$, 39$%$, and 58$%$ is attributed to vibrational excitations, electronic excitations, and ionization processes, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".