In vivo transgenic mutation assays
Bibliographic record
Abstract
Transgenic rodent gene mutation models provide quick and statistically reliable assays for mutations in the DNA from any tissue. For regulatory applications, assays should be based on neutral genes, be generally available in several laboratories, and be readily transferable. Five or fewer repeated treatments are inadequate to conclude that a compound is negative but more than 90 daily treatments may risk complications. A sampling time of 35 days is suitable for most tissues and chemicals, while shorter sampling times might be appropriate for highly proliferative tissues. For phage-based assays, 5 to 10 animals per group should be analyzed, assuming a spontaneous mutant frequency (MF) of approximately 3 x 10(-5) mutants/locus and 125,000-300,000 plaque or colony forming units (PFU or CFU) per tissue. Data should be generated for two dose groups but three should be treated, at the maximum tolerated dose (MTD), two-thirds the MTD, and one-third the MTD. Concurrent positive control animals are only necessary during validation, but positive control DNA must be included in each plating. Tissues should be processed and analyzed in a block design and the total number of PFUs or CFUs and the MF for each tissue and animal reported. Sequencing data would not normally be required but might provide useful additional information in specific circumstances. Statistical tests used should consider the animal as the experimental unit. Nonparametric statistical tests are recommended. A positive result is a statistically significant dose-response and/or statistically significant increase in any dose group compared to concurrent negative controls using an appropriate statistical model. A negative result is statistically nonsignificant with all mean MF within two standard deviations of the control.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".