Self-assembled manganese phthalocyanine nanoparticles with enhanced peroxidase-like activity for anti-tumor therapy
Bibliographic record
Abstract
The use of functional nanoparticles as peroxidase-like (POD-like) catalyst has recently become a focus of research in cancer therapy. Phthalocyanine is a macrocyclic conjugated metal ligand, which is expected to achieve a high POD-like catalytic activity, generating free radicals and inhibiting the proliferation of cancer cells. In this paper, we synthesized phthalocyanine nanocrystals with different structures through noncovalent self-assembly confined within micro-emulsion droplets, and manganese phthalocyanine (MnPc) possessing a metal-N-C active center was used as the building block. These nano-assemblies exhibit shape-dependent POD-like catalytic activities, because the emulsifier and MnPc co-mixed assembly reduced the close packing between MnPc molecules and exposed more active sites. The assembly had a water-dispersed nanostructure, which is conducive to accumulation at tumor sites through the enhanced permeability and retention effect (EPR). Because of a highly efficient microenvironmental response, the assembly showed higher catalytic activity only emerged under the acidic tumor-like microenvironment, but caused less damage to normal tissues in biomedical applications. In vivo and in vitro catalytic therapy tests showed excellent anti-tumor effects. This work explored a new way for the application of metal-organic macromolecules such as MnPc as nanozymes for catalytic tumor therapy.
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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".