Influence of track directions on the biological consequences in cells irradiated with high LET heavy ions
Bibliographic record
Abstract
PURPOSE: The impact of the damage distribution to cellular survival and chromosomal aberrations following high Linear Energy Transfer (LET) radiation was investigated. MATERIALS AND METHODS: High LET iron-ions (500 MeV/n, LET 200 keV/μm) were delivered to G1-phase synchronized Chinese Hamster Ovary (CHO) cells located at a vertical or horizontal angle relative to the ion beam in order to give same dose but different fluence and damage distribution. RESULTS: Horizontal irradiation produced DNA double-strand break (DSB) along each ion track represented as clustered lines, and vertical irradiation produced a greater fluence. The initial damages measured by premature chromosome condensation were equal per dose in both irradiation types. Horizontal irradiation proved to be less effective in cell killing than vertical at doses of more than 3 Gy. Vertical irradiation produced a higher number of metaphase chromosomal aberrations compared to horizontally irradiated samples. In particular, formation of exchange-type aberrations was the same, but that of deletion-type aberrations were significantly higher after vertical irradiation than horizontal irradiation. CONCLUSIONS: Therefore, we concluded that high fluence per dose is more effective than low fluence per dose to produce radiation-induced chromosomal aberrations and to kill exposed cells following high LET heavy-ion exposure.
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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) | 0.002 | 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".