High-LET Ion Radiolysis of Water: Visualization of the Formation and Evolution of Ion Tracks and Relevance to the Radiation-Induced Bystander Effect
Bibliographic record
Abstract
Ionizing radiation-induced bystander effects, commonly observed in cell populations exposed to high-linear energy transfer (LET) radiations, are initiated by damage to a cellular molecule which then gives rise to a toxic signal exported to neighboring cells not directly hit by radiation. A major goal in studies of this phenomenon is the identification of this initial radiation-induced lesion. Liquid water being the main constituent of biological matter, reactive species produced by water radiolysis in the cellular environment are likely to be major contributors to the induction of this lesion. In this context, the radiation track structure is of crucial importance in specifying the precise location and identity of all the radiolytic species and their subsequent signaling or damaging effects. We report here Monte Carlo track structure simulations of the radiolysis of liquid water by four different impacting ions 1H+, 4He2+, 12C6+ and 20Ne10+, with the same LET ( approximately 70 keV/ microm). The initial radial distribution profiles of the various water decomposition products (eaq(-), *OH, H*, H2 and H2O2) for the different ions considered are presented and discussed briefly in the context of track structure theory. As an example, the formation and temporal evolution of simulated 24 MeV 4He2+ ion tracks (LET approximately 26 keV/microm) are reported for each radiolytic species from 1 ps to 10 micros. The calculations reveal that the ion track structure is completely lost by approximately 1 micros.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".