Seeing, Targeting and Delivering with Upconverting Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it