Development of Low Radiation Dose Biomarkers: A Commentary on Whether Non-targeted Effects Need to Be Considered
Bibliographic record
Abstract
The issue of determining likely outcomes after low dose exposure to radiation is complex and controversial. Currently, the linear no-threshold (LNT) model is used to justify the linear extrapolation of (adverse) outcomes from high doses, where effects are clearly seen, to low doses, where effects are very difficult to detect and even more difficult to ascribe to the measured radiation exposure. Among the factors hindering the development of a more precise system are the lack of reliable predictors of system health. While biomarkers indicating the health of individual cells or organisms exist, they fail at low doses due to the complexity of cause-effect relationships and the multiple factors contributing "stress" to the system as a whole (whether "whole" is a whole organism, a population or an ecosystem). Approaches to capture this complexity include adverse outcome pathway (AOP) analysis, which looks at multiple levels of organization from gene to ecosystem. In this commentary, we discuss the role of non-targeted effects (NTE) such as genomic instability and bystander effects. These mechanisms involve transmission of information between different levels of organization. In the case of BE, signals from exposed to unexposed cells or organisms coordinate response at higher levels of organization, permitting population responses to radiation to be identified and, potentially, mitigated. Genomic instability is more complex as it involves not only signaling but also trans-generational transmission of genetic or epigenetic changes and may lead to long-term adaptive evolution. GI may also be involved in memory or legacy effects, which contribute a further component to the dose effect measured in legacy sites. Our recent analysis of the contributions of memory and legacy effects to the total effect using data sets from Chernobyl and Fukushima (voles, birds and butterflies) suggests this type of analysis may help reduce uncertainties over laboratory-to-field extrapolations. A focus on novel but widespread NTE mechanistic pathways may open the way to successful prophylaxis and development of new biomarkers for better risk assessment after low dose exposures.
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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 | 0.044 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.039 | 0.059 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".