Near-Infrared Light-Accelerated Bioorthogonal Drug Uncaging and Photothermal Ablation by Anisotropic Pd@Au Plasmonic Nanorods
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Selective activation of chemotherapeutics at the tumor site via bioorthogonal catalysis is a promising strategy to reduce collateral damage to healthy tissues and organs. Despite significant advances in this field, targeted drug activation by transition-metal catalysts is still limited by insufficient spatiotemporal control over the metal-mediated uncaging process. Herein, we report the development of anisotropic Pd@Au plasmonic nanorods with the capacity to accelerate dealkylation reactions under near-infrared (NIR) irradiation, thereby enabling precise control over when and where these catalytic devices are switched on. We also show that the stability and in cellulo chemical properties of Pd@Au nanorods are enhanced by Au–S functionalization with PEGylated phospholipids and report the development of a novel masking group for prodyes and prodrugs: the POxOC group, designed to improve physicochemical properties and the rate of the Pd-triggered dye/drug release process. NIR-photoactivation of lipo-Pd@Au nanorods is able to catalyze the uncaging of inactive drug precursors and release heat to the environment, killing cancer cells in culture and xenografted in zebrafish. This work provides a novel targeted strategy for photothermal chemotherapy by NIR-laser focalization.
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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) | 0.001 | 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".