High-LET Ion Radiolysis of Water: Oxygen Production in Tracks
Bibliographic record
Abstract
It is known that molecular oxygen is a product of the radiolysis of water with high-linear energy transfer (LET) radiation, a result that is of particular significance in radiobiology and of practical relevance in radiotherapy. In fact, it has been suggested that the radiolytic formation of an oxygenated microenvironment around the tracks of high-LET heavy ions is an important factor in their enhanced biological efficiency in the sense that this may be due to an "oxygen effect" by O(2) produced by these ions in situ. Using Monte Carlo track simulations of pure, deaerated water radiolysis by 4.8 MeV (4)He(2+) (LET approximately 94 keV/microm) and 24 MeV (12)C(6+) (LET approximately 490 keV/microm) ions, including the mechanism of multiple ionization of water, we have calculated the yields and concentrations of O(2) in the tracks of these irradiating ions as a function of time between approximately 10(-12) and 10(-5) s at 25 and 37 degrees C. The track oxygen concentrations obtained compare very well with O(2) concentrations estimated from the "effective" amounts of oxygen that are needed to produce the observed reduction in oxygen enhancement ratio (OER) with LET (assuming this decrease is attributable to the sole radiolytic formation of O(2) in the tracks). For example, for 24 MeV (12)C(6+) ions, the initial track concentration of O(2) is estimated to be more than three orders of magnitude higher than the oxygen levels present in normally oxygenated and hypoxic tumor regions as well as in normal human cells. Such results, which largely plead in favor of the "oxygen in the heavy-ion track" hypothesis, could explain at least in part the greater efficiency of high-LET radiation for cell inactivation (at equal radiation dose).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".