The impact of low-dose gamma radiation on immune modulation in a mouse model of spontaneous mammary gland tumorigenesis
Bibliographic record
Abstract
Understanding the impacts of low-dose ionizing radiation exposure has significant public health implications. However, the effects of low-dose ionizing radiation on immune modulation and cancer progression remain contentious. This study aimed to investigate the impact of chronic low-dose gamma radiation on mammary tumorigenesis and immune homeostasis using a transgenic mouse model. Female MMTV-neu transgenic mice were exposed to continuous whole-body 60 Co gamma radiation over a period of 56 days, thereby receiving cumulative absorbed doses of 10, 100 and 2,000 mGy. Mice were analyzed at 3.5, 6 and 8 months of age for changes in immune cell composition and function, as well as tumor development. We found that mice exposed to LDR exhibited transient increases in NK cell frequency, along with improved IFN-γ production following ex vivo stimulation. Notably, the expression of NKG2D on NK cells was upregulated following LDR exposure. Low-dose radiation also modulated inflammatory cytokine profiles and immune cell populations, such as macrophages and myeloid-derived suppressor cells. Despite these immune changes, the overall impact on tumorigenesis was minimal. Although our data indicated that the LDR treatment did not impact survival and cancer progression, the observed results of NK cell proportion, activation and function provide evidence of the stimulatory effects of LDR on NK cells. These findings aim to contribute to health risk assessments and advise radiation protection regulations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".