A new transgenic mouse line to image chemically induced p53 activation <i>in vivo</i>
Bibliographic record
Abstract
Monitoring p53 transcriptional activity to identify genotoxic damages induced by drugs has been proposed and validated in vitro. However, this methodology is by design limited to the cell line tested. In this study, we have fully validated a luciferase-based p53-reporter system in vitro and in vivo. We generated a mouse transgenic line to monitor non-invasively p53 activation in response to chemically induced DNA damage. Doxorubicin was used as a drug of known toxicity to validate our model. Reporter gene expression was measured using bioluminescence imaging. In females, a weak p53 luciferase activity driven by a p53-responsive promoter was detectable in the oral cavity region after doxorubicin treatment. In males, the signal increased in the lower abdominal region. Imaging of various organs revealed that the luciferase activity was mainly generated from the testes. Immunohistology demonstrated that the cells in the seminiferous tubules were damaged by the drug and confirmed that they were luciferase and p53 positive. Therefore, these transgenic mice could provide a powerful tool to predict, map and characterize at the organ and cellular levels the toxicity of compounds and help to develop new therapeutic agents in humans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".