History of bystander effects research 1905-present; what is in a name?
Bibliographic record
Abstract
PURPOSE: This review, which arose from a Radiation Research Society History symposium, traces the history of 'bystander effects' or 'indirect effects'(also known as 'abscopal effects', 'clastogenic effects' and more recently 'the secretosome'). In 1905, Murphy first drew attention to effects caused by the injection of irradiated cells into animals. In the present day, bystander effects are seen as part of the secretosome, where they coordinate responses to stressors at the tissue, organism, and population level. The review considers the history and also the reasons why this process of information exchange/communication appears to have been discovered and forgotten several times. The review then considers the evolution of our understanding of the mechanisms and what relevance these effects may have in radiation protection and radiotherapy. CONCLUSIONS: The authors conclude that the phenomenon currently described as a 'bystander effect' has been described under a variety of different names since 1905. However recent advances in biology have made it possible to investigate mechanisms and potential impacts more fully. This has led to the current upsurge in research into this effect of radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".