Transient hypoxia in water irradiated by swift carbon ions at ultra-high dose rates: implication for FLASH carbon-ion therapy
Bibliographic record
Abstract
Large doses of ionizing radiation delivered to tumors at ultra-high dose rates (i.e., in a few milliseconds) paradoxically spare the surrounding healthy tissue while preserving anti-tumor activity (compared with conventional radiotherapy delivered at much lower dose rates). This new modality is known as “FLASH radiotherapy” (FLASH-RT). Although the molecular mechanisms underlying FLASH-RT are not yet fully understood, it has been suggested that radiation delivered at high dose rates spares normal tissue via oxygen depletion followed by subsequent radioresistance of the irradiated tissue. To date, FLASH-RT has been studied using electrons, photons, and protons in various basic biological experiments, pre-clinical studies, and recently in a human patient. However, the efficacy of heavy ions, such as energetic carbon ions, under FLASH-RT conditions remains unclear. Given that living cells and tissues consist mainly of water, we set out to study, from a pure radiation chemistry perspective, the effects of ultra-high dose rates on the transient yields and concentrations of radiolytic species formed in water irradiated by 300-MeV per nucleon carbon ions (LET ∼ 11.6 keV/µm). This mimics irradiation in the “plateau” region of the depth–dose distribution of ions, i.e., in the “normal” tissue region in which the LET is rather low. We used Monte Carlo simulations of multiple, simultaneously interacting radiation tracks together with an “instantaneous pulse” irradiation model. Our calculations show a pronounced oxygen depletion around 0.2 μs, strongly suggesting, as with electrons, photons, and protons, that irradiation with energetic carbon ions at ultra-high dose rates is suitable for FLASH-RT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".