Evaluation of Azathioprine-Induced Cytotoxicity in an<i>In Vitro</i>Rat Hepatocyte System
Bibliographic record
Abstract
Azathioprine (AZA) is widely used in clinical practice for preventing graft rejection in organ transplantations and various autoimmune and dermatological diseases with documented unpredictable hepatotoxicity. The potential molecular cytotoxic mechanisms of AZA towards isolated rat hepatocytes were investigated in this study using "Accelerated Cytotoxicity Mechanism Screening" techniques. The concentration of AZA required to cause 50% cytotoxicity in 2 hrs at 37°C was found to be 400 μM. A significant increase in AZA-induced cytotoxicity and reactive oxygen species (ROS) formation was observed when glutathione- (GSH-) depleted hepatocytes were used. The addition of N-acetylcysteine decreased cytotoxicity and ROS formation. Xanthine oxidase inhibition by allopurinol decreased AZA-induced cytotoxicity, ROS, and hydrogen peroxide (H2O2) formation and increased % mitochondrial membrane potential (MMP). Addition of N-acetylcysteine and allopurinol together caused nearly complete cytoprotection against AZA-induced hepatocyte death. TEMPOL (4-hydroxy-2,2,6,6-tetramethylpiperidin-1-oxyl), a known ROS scavenger and a superoxide dismutase mimic, and antioxidants, like DPPD (N,N'-diphenyl-p-phenylenediamine), Trolox (a water soluble vitamin E analogue), and mesna (2-mercaptoethanesulfonate), also decreased hepatocyte death and ROS formation. Results from this study suggest that AZA-induced cytotoxicity in isolated rat hepatocytes may be partly due to ROS formation and GSH depletion that resulted in oxidative stress and mitochondrial injury.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".