Novel Mechanisms of Protection Against Acetaminophen Hepatotoxicity in Mice by Glutathione and N-Acetylcysteine
Bibliographic record
Abstract
UNLABELLED: Acetaminophen (APAP) overdose is a major cause of acute liver failure. The glutathione (GSH) precursor N-acetylcysteine (NAC) is used to treat patients with APAP overdose for up to 48 hours. Although it is well established that early treatment with NAC can improve the scavenging of the reactive metabolite N-acetyl-p-benzoquinone imine, protective mechanisms at later times remain unclear. To address this issue, fasted C3Heb/FeJ mice were treated with 300 mg/kg APAP and then received intravenously 0.65 mmol/kg GSH or NAC at 1.5 hours after APAP. The animals were sacrificed at 6 hours. APAP alone caused severe liver injury with peroxynitrite formation and DNA fragmentation, all of which was attenuated by both treatments. However, GSH (-82%) was more effective than NAC (-46%) in preventing liver injury. Using nuclear magnetic resonance spectroscopy to measure tissue adenosine triphosphate (ATP) levels and the substrate flux through the mitochondrial Krebs cycle, it was observed that the reduced liver injury correlated with accelerated recovery of mitochondrial GSH content, maintenance of ATP levels, and an increased substrate supply for the mitochondrial Krebs cycle compared with APAP alone. NAC treatment was less effective in recovering ATP and mitochondrial GSH levels and showed reduced substrate flux through the Krebs cycle compared with GSH. However, increasing the dose of NAC improved the protective effect similar to GSH, suggesting that the amino acids not used for GSH synthesis were used as mitochondrial energy substrates. CONCLUSION: Delayed treatment with GSH and NAC protect against APAP overdose by dual mechanisms-that is, by enhancing hepatic and mitochondrial GSH levels (scavenging of reactive oxygen and peroxynitrite)-and by supporting the mitochondrial energy metabolism.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".