Factors Influencing Effects of Low-dose Radiation Exposure
Bibliographic record
Abstract
ABSTRACT: It is now well accepted that the mechanisms induced by low-dose exposures to ionizing radiation (LDR) are different from those occurring after high-dose exposures. However, the downstream effects of these mechanisms are unclear as are the quantitative relationships between exposure, effect, harm, and risk. In this paper, we will discuss the mechanisms known to be important with an overall emphasis on how so-called "non-targeted effects" (NTE) communicate and coordinate responses to LDR. Targeted deposition of ionizing radiation energy in cells causing DNA damage is still regarded as the dominant trigger leading to all downstream events whether targeted or non-targeted. We regard this as an over-simplification dating back to formal target theory. It ignores that last 100 y of biological research into stress responses and signaling mechanisms in organisms exposed to toxic substances, including ionizing radiation. We will provide evidence for situations where energy deposition in cellular targets alone cannot be plausible as a mechanism for LDR effects. An example is where the energy deposition takes place in an organism not receiving the radiation dose. We will also discuss how effects after LDR depend more on dose rate and radiation quality rather than actual dose, which appears rather irrelevant. Finally, we will use recent evidence from studies of cataract and melanoma induction to suggest that after LDR, post-translational effects, such as protein misfolding or defects in energy metabolism or mitochondrial function, may dominate the etiology and progression of the disease. A focus on such novel 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".