Investigation of Non-Linear Adaptive Responses and Split Dose Recovery Induced by Ionizing Radiation in Three Human Epithelial Derived Cell Lines
Bibliographic record
Abstract
Two almost completely exclusive fields in radiobiology deal with splitting doses of radiation and comparing the effect to a similar total dose given in one exposure. In radiotherapy, dose "fractionation" is used to "spare" normal tissue and in the low dose field, the adaptive response is well documented as a phenomenon where a small "priming" dose administered before the larger "challenge" dose reduces the effect of the large dose. There have been very few studies where these fields overlap, thus it is not possible to ascertain whether common or distinct mechanisms underlie both phenomena but this is certainly an interesting question and relevant to our understanding of high and low dose radiobiology. This paper presents data for three human cell lines with varying p53 status and radiation responses, treated at a range of times between first and second dose and for 3 different first doses (0.1, 0.5 and 2Gy). The data show that time between doses is critical. Protective (adaptive) effects were seen in each cell line but most prominently in the malignant HT 29 cell line. Surprisingly none of the cell lines showed pronounced split dose recovery. This suggests different mechanisms may underlie the two phenomena.
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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 | 0.001 |
| 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".