Seeing, Targeting and Delivering with Upconverting Nanoparticles
Bibliographic record
Abstract
Efficient control over drug release is critical to increasing drug efficacy and avoiding side effects. An ideal drug delivery system would deliver drugs in the right amount, at the right location and at the right time noninvasively. This can be achieved using light-triggered delivery: light is noninvasive, spatially precise and safe if appropriate wavelengths are chosen. However, the use of light-controlled delivery systems has been limited to areas that are not too deep inside the body because ultraviolet (UV) or visible (Vis) light, the typical wavelengths used for photoreactions, have limited penetration and are toxic to biological tissues. The advent of upconverting nanoparticles (UCNPs) has made it possible to overcome this crucial challenge. UCNPs can convert near-infrared (NIR) radiation, which can penetrate deeper inside the body, to shorter wavelength NIR, Vis and UV radiation. UCNPs have been used as bright, in situ sources of light for on-demand drug release and bioimaging applications. These remote-controlled, NIR-triggered drug delivery systems are especially attractive in applications where a drug is required at a specific location and time such as in anesthetics, postwound healing, cardiothoracic surgery and cancer treatment. In this Perspective, we discuss recent progress and challenges as well as propose potential solutions and future directions, especially with regard to their translation to the clinic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".