Bioinspired Design of Single-Atom Micellar Nanozymes Facilitates Internal Environment-Driven Synergistic Therapy against Lymphoma
Bibliographic record
Abstract
The binding and catalytic sites of natural enzymes play a decisive role in enzymatic processes. Constructing bioinspired catalytic centers and advancing nanozyme-mediated cancer therapeutics represent a significant yet challenging frontier. Herein, we have developed tumor-targeting micellar single-atom nanozymes (Fe(II)NC-SAz-E5 micelles) through a rational design that integrates tumor microenvironment (TME) responsivity and active targeting for synergistic anti-Non-Hodgkin’s lymphoma (NHL) therapy. The Fe(II) ions were precisely anchored into PLGA-PEG-NH 2 self-assembled micelle-DOTA-E5 complex via DOTA chelation, forming FeN 4 (COOH) active sites that mimic natural peroxidase with remarkable catalytic efficiency. Guided by the tumor-targeting peptide E5, these micelles preferentially accumulate in tumor tissues and inhibit metastasis by suppressing the CXCR4/CXCL12 signaling axis. Moreover, tumor cells are effectively eliminated through a self-sustaining cycle of “substrate supply-catalytic amplification-metabolic intervention,” leveraging the elevated levels of H 2 O 2, GSH, and glucose in the TME. Extensive in vitro and in vivo studies demonstrated the micelles’ potent catalytic activity, significant inhibition of lymphoma progression, and strong antimetastatic effects. Notably, the use of FDA-approved PLGA and clinically established DOTA chelation highlights the strong translational potential of this platform for future clinical applications.
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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) | 0.000 | 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".