Near-Infrared-Triggered Anticancer Drug Release from Upconverting Nanoparticles
Bibliographic record
Abstract
Targeted drug delivery using functional nanoparticles has provided new strategies for improving therapeutic efficacy while concurrently minimizing toxicity. Photodynamic therapy is an approach that offers control of drug delivery by use of an external photon source to allow active therapeutic release to a target area. Upconverting nanoparticles (UCNPs) have potential to operate as integral components of photodynamic therapeutic platforms based on the resonant absorption of near-infrared (NIR) radiation and emission at shorter wavelengths. NIR radiation is minimally absorbed and scattered by biological tissues, and the NIR excitation of UCNPs can generate anti-Stokes emission in the ultraviolet-visible wavelength range at intensities that can be used to trigger cleavage of bonds linking therapeutics at the nanoparticle interface. Herein, we describe an investigation of photocleavage at the surface of UCNPs to release the chemotherapeutic 5-fluorouracil (5-FU). Core-shell UCNPs composed of a β-NaYF4: 4.95% Yb, 0.08% Tm core and a β-NaYF4 shell were coated with o-phosphorylethanolamine ligands and coupled to an o-nitrobenzyl (ONB) derivative of 5-FU. NIR excitation of the UCNPs resulted in photoluminescence (PL) emission bands centered at 365, 455, and 485 nm. The UV-blue PL was in resonance with the absorption band of the ONB-FU derivative resulting in photocleavage and subsequent release of the 5-FU drug from the UCNPs for these in vitro studies. The release of 5-FU was complete in <14 min using a NIR laser source centered at 980 nm that operated at a power of <100 mW. The efficiency of triggered release was as high as 77% of the total ONB-FU conjugate, while the rate of drug release could be tuned with the laser power output. This work provides an important first step in the development of a UCNP platform capable of targeted chemotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".