Photocleavable Hydrogel-Coated Upconverting Nanoparticles: A Multifunctional Theranostic Platform for NIR Imaging and On-Demand Macromolecular Delivery
Bibliographic record
Abstract
Lanthanide-doped upconverting nanoparticles (UCNPs) have emerged as excellent nanotransducers for converting longer wavelength near-infrared (NIR) light to shorter wavelengths spanning the ultraviolet (UV) to the visible (Vis) regions of the spectrum via a multiphoton absorption process, known as upconversion. Here, we report the development of NIR to UV-Vis-NIR UCNPs consisting of LiYF4:Yb(3+)/Tm(3+)@SiO2 individually coated with a 10 ± 2 nm layer of chitosan (CH) hydrogel cross-linked with a photocleavable cross-linker (PhL). We encapsulated fluorescent-bovine serum albumin (FITC-BSA) inside the gel. Under 980 nm excitation, the upconverted UV emission cleaves the PhL cross-links and instantaneously liberates the FITC-BSA under 2 cm thick tissue. The release is immediately arrested if the excitation source is switched off. The upconverted NIR light allows for the tracking of particles under the tissue. Nucleus pulposus (NP) cells cultured with UCNPs are viable both in the presence and in the absence of laser irradiation. Controlled drug delivery of large biomolecules and deep tissue imaging make this system an excellent theranostic platform for tissue engineering, biomapping, and cellular imaging applications.
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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".