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Record W2080504905 · doi:10.1080/15401420490507512

The Bystander Effect: Recent Developments and Implications for Understanding the Dose Response

2004· article· en· W2080504905 on OpenAlexaff
R. E. J. Mitchel

Bibliographic record

VenueNonlinearity in Biology Toxicology Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsBystander effectIonizing radiationAdaptive responseBiologyIn vitroCellCell typeToxicologyCell biologyNeuroscienceMedicineImmunologyIrradiationGeneticsPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.394
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2004
Admission routes1
Has abstractyes

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