Modeling xenobiotic susceptibility to hepatotoxicity using an in vitro oxidative stress inflammation model
Bibliographic record
Abstract
Evidence suggests xenobiotic exposure during periods of inflammation can increase an individual's susceptibility to toxicity. The present study aimed to validate an in-vitro inflammatory model to identify compounds that increase hepatotoxicity during inflammation. Using freshly isolated hepatocytes exposed to a low nontoxic flow of H2O2 using glucose (G) and glucose oxidase (GO) and supplementing it with either peroxidase or Fe(II), the effects of inflammation on 2 classes of drugs known to cause hepatotoxicity were examined: nitroaromatics (nimesulide, nilutamide, flutamide) and aromatic amines (clozapine, thioridazine). Co-incubation with G/GO and the nitroaromatics increased toxicity that was further increased when peroxidase was present. While the aromatic amines did not increase cytotoxicity with G/GO alone, they demonstrated significant increases in cytotoxicity when incubated with peroxidase+G/GO. Liver injury is commonly observed with alcohol abusers; therefore, the effects of inflammation on ethanol, and its metabolite acetaldehyde, were observed. Both ethanol and acetaldehyde increased cytotoxicity, which was further increased when Fe(II) was present. These results implicate H2O2, a cellular mediator of inflammation, as a potential risk factor for hepatotoxicity. A H2O2-enhanced hepatocyte-system in the presence of peroxidase or Fe(II) may prove useful for a more robust screening of xenobiotics for assessing potential toxicity associated with inflammation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".