Catalytic Nanomedicine: Antioxidant Action and Clinical Benefits Using Cerium Oxide Nanoparticles
Bibliographic record
Abstract
Oxidative stress plays a central role in the pathogenesis of various chronic diseases by driving inflammation, cellular damage, and metabolic dysfunction. The imbalance between reactive oxy-gen species (ROS) production and antioxidant defenses contributes to neurodegenerative, cardi-ovascular, and inflammatory disorders, highlighting the urgent need for innovative therapeutic strategies. In this context, catalytic nanomedicine has emerged as a promising approach to mitigate oxidative damage through nanocatalysts that mimic enzymatic antioxidant activity. This review explores recent advances in antioxidant nanocatalysts, particularly metal oxide nanoparticles such as cerium oxide, emphasizing their biochemical mechanisms, therapeutic applications, and potential for clinical translation. This nanomaterial has demonstrated the ability to modulate redox homeostasis, reduce inflammatory markers, and preserve cellular integrity in preclinical models. Moreover, multifunctional nanocatalysts offer advantages such as enhanced stability, tunable catalytic activity, and the potential for targeted delivery, making them compelling candi-dates for precision medicine. However, despite their potential, significant challenges remain, parti-cularly concerning biocompatibility, long-term safety, and large-scale production. Further re-search is needed to optimize physicochemical properties, improve bioavailability, and ensure regulatory compliance. Therefore, addressing these limitations is essential to accelerate the trans-lation of experimental findings into clinical practice, paving the way for advanced nano-therapies with extensive biomedical applications that utilize catalytic mechanisms to modulate redox balance.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".