Pt‐Decorated Gold Nanoflares for High‐Fidelity Phototheranostics: Reducing Side‐Effects and Enhancing Cytotoxicity toward Target Cells
Bibliographic record
Abstract
Functionalized with the Au-S bond, gold nanoflares have emerged as promising candidates for theranostics. However, the presence of intracellular abundantly biothiols compromises the conventional Au-S bond, leading to the unintended release of cargoes and associated side-effects on non-target cells. Additionally, the hypoxic microenvironment in diseased regions limits treatment efficacy, especially in photodynamic therapy. To address these challenges, high-fidelity photodynamic nanoflares constructed on Pt-coated gold nanoparticles (Au@Pt PDNF) were communicated to avoid false-positive therapeutic signals and side-effects caused by biothiol perturbation. Compared with conventional photodynamic gold nanoflares (AuNP PDNF), the Au@Pt PDNF were selectively activated by cancer biomarkers and exhibited high-fidelity phototheranostics while reducing side-effects. Furthermore, the ultrathin Pt-shell catalysis was confirmed to generate oxygen which alleviated hypoxia-related photodynamic resistance and enhanced the antitumor effect. This design might open a new venue to advance theranostics performance and is adaptable to other theranostic nanomaterials by simply adding a Pt shell.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".