Radiation-induced Bystander Effects: Are They Good, Bad or Both?
Bibliographic record
Abstract
Our current knowledge of the mechanisms underlying the induction of bystander effects by low dose-low linear-energy-transfer ionising radiation is reviewed, and the question of how bystander effects may be related to observed adaptive responses, systemic genomic instability or other effects of low doses exposures is considered. Bystander effects appear to be the result of a generalised stress response in tissues or cells. The signals may be produced by all exposed cells but the response may require a quoram in order to be expressed. The major response involving low LET radiation exposure discussed in the existing literature is a death response, which has many characteristics of apoptosis but may be detected in cell lines without p53 expression. While a death response might appear to be adverse, it can in fact be protective and remove damaged cells from the population. Since many cell populations carry damaged cells without being exposed to radiation ('background damage') low doses exposures might cause removal of cells damaged by agents other than the test dose of radiation, which would lead to the production of 'u- or n-shaped' dose-response curves. The level of harmful or beneficial response would then be related to the background damage carried by the cell population and the genetic programme determining response to damage. This model may be important when attempting to predict the consequences of mixed exposures involving radiation and other environmental stressors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".