Hepatic effects of aminoglutethimide: A model aromatic amine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Primary aromatic amine drugs are structural alerts in drug development because of their association with a high incidence of idiosyncratic drug reactions (IDRs). If biomarkers could be found that predict IDR risk, it would have a major impact on drug development. Previous attempts to do this through screening of hepatic gene expression profiles in rodents treated with aromatic amine drugs found limited changes. Of the drugs studied, aminoglutethimide (AMG) induced the most changes, and this led to a more comprehensive study of its effects on the liver. Brown Norway rats treated with AMG for up to 14 days showed only a transient elevation of glutamate dehydrogenase. Pathway-specific PCR arrays found few AMG-induced gene changes associated with an immune response and, of these changes, the majority were involved with innate immunity such as Tlr2, Ticam2, CD14, and C3. AMG treatment also led to significant changes in the apoptosis and mitochondrial panel of genes. It was recently found that AMG does induce significant changes in the bone marrow of rats, and agranulocytosis is a common IDR caused by AMG. In contrast, liver injury is not a common IDR associated with AMG. Therefore, the liver may be able to effectively deal with AMG reactive metabolites, and changes observed in this study may be involved in adaptation. Myeloperoxidase is also known to be able to oxidize aromatic amines to reactive metabolites, and these observations suggest that metabolism outside of the liver may be important for the mechanism of aromatic amine-induced IDRs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it