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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".