Catalytic properties and biomedical applications of cerium oxide nanoparticles
Bibliographic record
Abstract
Cerium oxide nanoparticles (Nanoceria) have shown promise as catalytic antioxidants in the test tube, cell culture models and animal models of disease. However given the reactivity that is well established at the surface of these nanoparticles, the biological utilization of Nanoceria as a therapeutic still poses many challenges. Moreover the form that these particles take in a biological environment, such as the changes that can occur due to a protein corona, are not well established. This review aims to summarize the existing literature on biological use of Nanoceria, and to raise questions about what further study is needed to apply this interesting catalytic material to biomedical applications. These questions include: 1) How does preparation, exposure dose, route and experimental model influence the reported effects of Nanoceria in animal studies? 2) What are the considerations to develop Nanoceria as a therapeutic agent in regards to these parameters? 3) What biological targets of reactive oxygen species (ROS) and reactive nitrogen species (RNS) are relevant to this targeting, and how do these properties also influence the safety of these nanomaterials?
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".