Chitosan Nanoparticles as a Therapeutic Agent for Mitigating Paracetamol-Induced Liver Damage in White Male Albino Rats
Bibliographic record
Abstract
High paracetamol doses can cause liver damage, but chitosan nanoparticles (ChNPs), known for their biocompatibility, biodegradability, anti-inflammatory, and antioxidant properties, may offer potential therapeutic benefits. This study investigated the protective effects of ChNPs in mitigating paracetamol-induced liver damage in white male albino rats. Chitosan nanoparticles were synthesized and characterized using scanning electron microscopy, atomic force microscopy, and UV-Vis spectroscopy. Sixty adult male white albino rats (200–250 g) were randomly assigned to six groups and treated orally for three months. Groups received distilled water (Group I), 15 mg/kg ChNPs (Group II), 500 mg/kg (Group III), or 1000 mg/kg (Group IV) paracetamol or combinations of 500 mg/kg (Group V) or 500 mg/kg (Group VI) paracetamol with 15 mg/kg ChNPs. Liver tissues were histologically examined, and DNA damage was assessed. The results showed that ChNPs (43–80 nm) exhibited smooth, spherical morphology with improved dispersion. Prolonged oral administration of paracetamol at both doses induced significant histopathological changes, including fibrosis, congestion, and inflammatory infiltration in liver tissues. Co-administration with ChNPs preserved liver architecture, reducing morphological abnormalities. The comet assay showed significant DNA strand breaks in paracetamol-treated rats, indicated by increased tail DNA% and tail length. Chitosan nanoparticles mitigated DNA damage, likely due to their antioxidant and anti-inflammatory properties. The study’s findings revealed that ChNPs exhibited a protective effect against paracetamol-induced hepatotoxicity, mitigating both histopathological and genetic alterations. These findings underscore the potential therapeutic application of ChNPs in preventing drug-induced liver damage and support their broader use in nanomedicine.
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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) | 0.000 | 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".