Upper Dose Thresholds for Radiation-Induced Adaptive Response against Cancer in High-Dose-Exposed, Cancer-Prone, Radiation-Sensitive<i>Trp53</i>Heterozygous Mice
Bibliographic record
Abstract
Trp53 heterozygous mice are radiation-sensitive and cancer-prone. Groups of 7-8-week-old female Trp53 heterozygous mice were exposed to 4 Gy of 60Co gamma radiation at high (0.5 Gy/min) or low (0.5 mGy/min) dose rate. Other groups received 10 or 100 mGy at low dose rate 24 h prior to the 4-Gy dose. Tumor frequency and latency were measured over the animals' life span. Exposure to 10 mGy prior to 4 Gy resulted in a small (approximately 5%) but significant life-span regain and increased latency (approximately 9%) for all malignant tumors taken together, but 100 mGy further reduced life span slightly (approximately 7%). Latency responses were tumor type-specific. The prior 10-mGy exposure resulted in a small (approximately 7%) regain in latency for lymphomas but no change in latency for spinal osteosarcomas. Increasing the adapting dose to 100 mGy eliminated the increase in lymphoma latency and further reduced life span (approximately 8%). A 10-mGy dose prior to 4 Gy at low dose rate had no effects. Adapting exposures had no significant effect on tumor frequency. We conclude that a single low dose induced a small protective response in vivo in Trp53+/- mice, reducing the carcinogenic effects of a subsequent large, high-dose-rate exposure by increasing tumor latency. The upper dose threshold at which low-dose protective effects gave way to detrimental effects was tumor type-specific, as found previously for spontaneous tumors in these same cancer-prone mice (Radiat. Res. 159, 320-327, 2003). However, the upper dose thresholds appear to be lower (below 100 mGy) for radiation-induced tumors than for the same tumors appearing spontaneously.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".