The Influence of <i>TRP53</i> in the Dose Response of Radiation-Induced Apoptosis, DNA Repair and Genomic Stability in Murine Haematopoietic Cells
Bibliographic record
Abstract
Apoptotic and DNA damage endpoints are frequently used as surrogate markers of cancer risk, and have been well-studied in the Trp53+/- mouse model. We report the effect of differing Trp53 gene status on the dose response of ionizing radiation exposures (0.01-2 Gy), with the unique perspective of determining if effects of gene status remain at extended time points. Here we report no difference in the dose response for radiation-induced DNA double-strand breaks in bone marrow and genomic instability (MN-RET levels) in peripheral blood, between wild-type (Trp53+/+) and heterozygous (Trp53+/-) mice. The dose response for Trp53+/+ mice showed higher initial levels of radiation-induced lymphocyte apoptosis relative to Trp53+/- between 0 and 1 Gy. Although this trend was observed up to 12 hours post-irradiation, both genotypes ultimately reached the same level of apoptosis at 14 hours, suggesting the importance of late-onset p53-independent apoptotic responses in this mouse model. Expected radiation-induced G1 cell cycle delay was observed in Trp53+/+ but not Trp53+/-. Although p53 has an important role in cancer risk, we have shown its influence on radiation dose response can be temporally variable. This research highlights the importance of caution when using haematopoietic endpoints as surrogates to extrapolate radiation-induced cancer risk estimation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".