Nanozyme for tumor therapy: Surface modification matters
Bibliographic record
Abstract
Abstract As the next generation of artificial enzymes, nanozymes have shown unique properties compared to its natural counterparts, such as stability in harsh environment, low cost, and ease of production and modification, paving the way for its biomedical applications. Among them, tumor catalytic therapy mediated by the generation of reactive oxygen species (ROS) has made great progress mainly from the peroxidase‐like activity of nanozymes. Fe 3 O 4 nanozymes, the earliest type of nanomaterial discovered to possess peroxidase‐like activity, has consequently received wide attention for tumor therapy due to its ROS generation ability and tumor cell killing ability. However, inconsistent results of cytotoxicity were observed between different reports, and some even showed the scavenging of ROS in some cases. By collectively studying these inconsistent outcomes, we raise the question whether surface modification of Fe 3 O 4 nanozymes, either through affecting peroxidase activity or by affecting the biodistribution and intracellular fate, play an important role in its therapeutic effects. This review will go over the fundamental catalytic mechanisms of Fe 3 O 4 nanozymes and recent advances in tumor catalytic therapy, and discuss the importance of surface modification. Employing Fe 3 O 4 nanozymes as an example, we hope to provide an outlook on the improvement of nanozyme‐based antitumor activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".