Therapeutic effect of liposomal-N-acetylcysteine against acetaminophen-induced hepatotoxicity
Bibliographic record
Abstract
BACKGROUND: Acetaminophen (APAP) is an antipyretic analgesic drug that when taken in overdose causes depletion of glutathione (GSH) and hepatotoxicity. N-acetylcysteine (NAC) is the antidote of choice for the treatment of APAP toxicity; however, due to its short-half-life repeated dosing of NAC is required. PURPOSE: To determine whether a NAC-loaded liposomal formulation (Lipo-NAC) is more effective than the conventional NAC in protecting against acute APAP-induced hepatotoxicity. METHODS: Male Sprague-Dawley rats were challenged with an intragastric dose of APAP (850 mg/kg b.wt.); 4 h later, animals were administered saline, NAC, Lipo-NAC or empty liposomes and sacrificed 24 h post-APAP treatment. RESULTS: APAP administration resulted in hepatic injury as evidenced by increases in plasma bilirubin, alanine (AST) and aspartate (ALT) aminotransferase levels and tissue levels of lipid peroxidation and myeloperoxidase as well as decreases in hepatic levels of reduced GSH, GSH peroxidase and GSH reductase. Treatment of animals with Lipo-NAC was significantly more effective than free NAC in reducing APAP-induced hepatotoxicity. Histological evaluation showed that APAP caused periacinar hepatocellular apoptosis and/or necrosis of hepatocytes around the terminal hepatic venules which was reduced by NAC treatment, the degree of reduction being greater for Lipo-NAC. CONCLUSION: These data suggest that administration of Lipo-NAC ameliorated the APAP-induced hepatotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".