Effects of Chronic Low-Dose Internal Radiation on Immune-Stimulatory Responses in Mice
Bibliographic record
Abstract
The Linear-No-Threshold (LNT) model predicts a dose-dependent linear increase in cancer risk. This has been supported by biological and epidemiological studies at high-dose exposures. However, at low-doses (LDR ≤ 0.1 Gy), the effects are more elusive and demonstrate a deviation from linearity. In this study, the effects of LDR on the development and progression of mammary cancer in FVB/N-Tg(MMTVneu)202Mul/J mice were investigated. Animals were chronically exposed to total doses of 10, 100, and 2000 mGy via tritiated drinking water, and were assessed at 3.5, 6, and 8 months of age. Results indicated an increased proportion of NK cells in various organs of LDR exposed mice. LDR significantly influenced NK and T cell function and activation, despite diminishing cell proliferation. Notably, the expression of NKG2D receptor on NK cells was dramatically reduced at 3.5 months but was upregulated at later time-points, while the expression of NKG2D ligand followed the opposite trend, with an increase at 3.5 months and a decrease thereafter. No noticeable impact was observed on mammary cancer development, as measured by tumor load. Our results demonstrated that LDR significantly influenced the proportion, proliferation, activation, and function of immune cells. Importantly, to the best of our knowledge, this is the first report demonstrating that LDR modulates the cross-talk between the NKG2D receptor and its ligands.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".