Development of an <i>in vivo</i> Assay for Detection of Non-Targeted Radiation Effects
Bibliographic record
Abstract
An adaptive response may be defined as the effect of a small priming dose of radiation modifying the anticipated cellular response of the same tissues so as to alter the predicted response to a larger dose of radiation. We and others have demonstrated that at low radiation doses (less than 0.5 Gy) the lethal and mutational effect of the radiation is mainly, possibly entirely, due to the non-targeted effects. This is the dose range for priming doses in adaptive response protocols. In an associated presentation from our group, we demonstrate that the adaptive response may be explicable as a non targeted (bystander) response. In this paper we present data from exposed human patients, showing that a simple assay using blood can demonstrate variation in the extent and type of non-targeted effects and that exposure to radiation can modulate the subsequent non-targeted response to a later dose. Patients undergoing radiotherapy treatment for cancer gave blood samples immediately after the first dose, midway during and six weeks after therapy. The serum from these samples was harvested, diluted in tissue culture medium and added to reporter cells. The toxicity or growth promoting activity of the serum was measured using a clonogenic assay coupled with immunocytochemical measurement of various proteins involved in apoptosis or growth. There is already evidence that bystander effects are controlled by both genetic and epigenetic (lifestyle) factors. These data could support the development of a simple blood based assay to predict overall response of human subjects to low doses of radiation taking all the low dose factors into account.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".