Influence of small doses of various drug vehicles on acetaminophen-induced liver injury
Bibliographic record
Abstract
The biological effects of drug vehicles are often overlooked, often leading to artifacts in acetaminophen-induced liver injury assessment. Therefore, we decided to investigate the effect of dimethylsulfoxide, dimethylformamide, propylene glycol, ethanol, and Tween 20 on acetaminophen-induced liver injury. C57BL/6 male mice received a particular drug vehicle (0.6 or 0.2 mL/kg, i.p.) 30 min before acetaminophen administration (300 mg/kg, i.p.). Control mice received vehicle alone. Liver injury was assessed by measuring the concentration of alanine aminotransferase in plasma and observing histopathological changes. The level of reduced glutathione (GSH) was assessed by measuring total nonprotein hepatic sulfhydrils. Dimethylsulfoxide and dimethylformamide (at both doses) almost completely abolished acetaminophen toxicity. The higher dose of propylene glycol (0.6 mL/kg) was markedly protective, but the lower dose (0.2 mL/kg) was only slightly protective. These solvents also reduced acetaminophen-induced GSH depletion. Dimethylformamide was protective when given 2 h before or 1 h after acetaminophen administration, but was ineffective if given 2.5 h after acetaminophen. Ethanol at the higher dose (0.6 mL/kg) was partially protective, whereas ethanol at the lower dose (0.2 mL/kg) as well as Tween 20 at any dose had no influence. None of the vehicles (0.6 mL/kg) was hepatotoxic per se, and none of them was protective in a model of liver injury caused by D-galactosamine and lipopolysaccharide.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".