The Bystander Effect: Recent Developments and Implications for Understanding the Dose Response
Bibliographic record
Abstract
The bystander effect refers to the biological response of a cell resulting from an event in an adjacent or nearby cell. Such effects depend on intercellular communication and amplify the consequences of the original event. These responses are of particular interest in the assessment of ionizing radiation risk because at public or occupational exposure levels not every cell receives a radiation track. Current radiation protection regulations and practices are based on the assumption of a linear increase in risk with dose, including low doses where not all cells are hit. Mechanisms that amplify biological effects are inconsistent with these assumptions. Evidence suggests that there are two different bystander effects in mammalian cells. In one type, a radiation track in one cell leads to damaging, mutagenic, and sometimes lethal events in adjacent, unhit cells. In the other type, a radiation track in one cell leads to an adaptive response in bystander cells, increasing resistance to spontaneous or radiation-induced events. This paper describes some of the data for radiation-induced bystander effects in vitro and correlates that data with in vitro and in vivo observations of risk at low doses. The data suggest that protective effects, including beneficial bystander effects, outweigh detrimental effects at doses below about 100 mGy, but that the reverse is true above this threshold.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".