Low Doses of Radiation Increase the Latency of Spontaneous Lymphomas and Spinal Osteosarcomas in Cancer-Prone, Radiation-Sensitive<i>Trp53</i>Heterozygous Mice
Bibliographic record
Abstract
Mice heterozygous for Trp53 are radiation-sensitive and cancer-prone, spontaneously developing a variety of cancer types. Osteosarcomas in the spine lead to paralysis, while lymphomas lead rapidly to death, distinct events that provide objective measures of latency. The effects of a single low-dose (10 or 100 mGy), low-dose-rate (0.5 mGy/min) (60)Co gamma irradiation on lymphoma or spinal osteosarcoma frequency and latency, defined as time of death or of onset of paralysis, respectively, were examined. Compared to spontaneous lymphomas or to spinal osteosarcomas leading to paralysis in unexposed mice, an exposure of 7-8-week-old Trp53(+/-) mice to 10 or 100 mGy had no significant effect on tumor frequency, indicating no effect on tumor initiation. All tumors are therefore assumed to be of spontaneous origin. However, a 10-mGy exposure reduced the risk of both lymphomas and spinal osteosarcomas by significantly increasing tumor latency, indicating that the main in vivo effect of a low-dose exposure is a reduction in the rate at which spontaneously initiated cells progress to malignancy. The effect of this adaptive response persisted for the entire life span of all the animals that developed these tumors. Exposure to 100 mGy delayed lymphoma latency longer than the 10-mGy exposure. However, the 100-mGy dose increased spinal osteosarcoma risk by decreasing overall latency compared to unexposed control mice. That result suggested that this higher dose was in a transition zone between reduced and increased risk, but that the dose at which the transition occurs varies with the tumor type.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".