Facile Synthesis of Designer Shape-Defined Mesoporous Metal Nanoenzymes as Therapeutics for Diseases Involving Excessive Oxidative Stress
Bibliographic record
Abstract
Mesoporous metal nanomaterials (MMNs) have gained interest in biomedicine for their unique properties, but their potential is limited by the predominance of spherical shapes and the neglect of morphological effects on biological activity, which hinders the reasonable evaluation of morphology-dependent enzyme-like activities and biological behaviors and its further biomedical applications. It is therefore imperative to find an effective and facile method to design and prepare MMNs with novel, well-defined morphologies. Herein, we fabricated 3 mesoporous platinum nanoenzymes including sphere, rod, and bipyramid topologies [Au@mesoPt sphere, Au@mesoPt rod, and Au@mesoPt bipyramid nanoparticles (NPs), respectively] via a facile atomic layer deposition method using gold NPs (Au NPs) as the templated cores and Pluronic F127 as a structure-directing agent. The obtained Au@mesoPt NPs could enhance cellular uptake efficiency and prolong blood elimination half-lives, which helped more cancer cell spheroid permeation and accumulation at the disease sites post-injection. Au@mesoPt NPs could obviously alleviate atherosclerosis through reactive oxide species (ROS) scavenge due to its catalase-like activity and inhibition of pro-inflammatory cytokine release. Due to the role of metal nanoenzymes containing high-order-number ( Z ) elements as radiosensitizers, Au@mesoPt NPs have a distinct radiosensitizing on pancreatic cancer treatment. Among the shapes, Au@mesoPt bipyramids showed the best therapeutic efficacy in treating atherosclerosis and pancreatic cancer, likely due to their high aspect ratio, irregular surface, and anisotropy, which favor blood flow and cellular uptake. The tunable synthesis of shape-defined MMNs bodes well for other areas of application, including biosensors, surface-enhanced Raman scattering, surface plasmon resonance, hydrogen storage, catalysis, and electrotherapy.
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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".