Redox-Active Cerium Oxide Nanoparticles Protect Against Acetaminophen-Induced Acute Liver Injury by Modulating Oxidative Stress and Inflammatory Pathways
Bibliographic record
Abstract
Acute liver injury (ALI), often triggered by an acetaminophen (APAP) overdose, is characterized by severe oxidative stress, inflammation, and hepatocyte apoptosis. Current therapies, such as N -acetylcysteine (NAC), are limited by narrow treatment windows, highlighting the need for more effective antioxidant strategies. In this study, cerium oxide nanoparticles (CeO 2 NPs, nanoceria) were synthesized and comprehensively characterized using the transmission electron microscopy (TEM), dynamic light scattering method (DLS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) to confirm their branched morphology, high crystallinity, and mixed Ce 3+ /Ce 4+ valence states. Their enzyme-mimetic antioxidant activities were evaluated through superoxide, hydrogen peroxide, and hydroxyl radical scavenging assays. Nanoceria exhibited excellent cytocompatibility and effectively suppressed the generation of lipopolysaccharide (LPS)-induced reactive oxygen species (ROS) and lipid peroxidation and caspase-3-mediated apoptosis in macrophages. They also downregulated pro-inflammatory mediators (nitric oxide synthase (iNOS), TNF-α, IL-1β, and NLRP3) while enhancing anti-inflammatory markers (Arg1 and IL-10). In an APAP-induced ALI mouse model, nanoceria preferentially accumulated in the liver, alleviated oxidative stress and inflammation, and significantly reduced aspartate aminotransferase (AST) levels, showing hepatoprotective efficacy comparable to NAC. Nanoceria protect against APAP-induced ALI via synergistic antioxidative and anti-inflammatory mechanisms based on reversible Ce 3+ /Ce 4+ redox cycling. These findings underscore nanoceria’s potential as a next-generation nanotherapeutic for oxidative stress-related liver diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".