Isoniazid-induced liver injury and immune response in mice
Bibliographic record
Abstract
Isoniazid (INH) is associated with one of the highest incidences of idiosyncratic drug-induced liver failure of any commonly prescribed drug. The mechanism of this liver injury remains uncertain, and a valid animal model would greatly facilitate mechanistic studies. Most studies of INH-induced liver toxicity have been acute studies performed in rats with high doses of the drug, and this is very different from the idiosyncratic liver injury that occurs in humans. It has previously been demonstrated that covalent binding of INH in the liver of mice is greater than in rats and more like that in humans. Therefore, mice should be a better species in which to develop an animal model of INH-induced liver injury. Treatment of Cbl-b(-/-) and PD1(-/-) mice, which have impaired immune tolerance, resulted in greater injury than their C57BL/6 background, but not liver failure. This suggested that the injury was mediated by the adaptive immune system; however, Rag(-/-) mice, which do not have competent T- and B-cells, sustained more liver injury than C57BL/6 wild-type mice. This suggested that the adaptive immune system also played a protective role. INH treatment also led to a decrease in the inflammatory cytokines IL-1α and IL-12, which suggests that the drug may have immunosuppressive properties. In short, a mouse model was developed of INH-induced liver injury in which the immune system appears to play a both protective and pathogenic role, but this study was unable to develop a model of INH-induced liver failure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.002 |
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".