Phytoglycogen Encapsulation of Lanthanide‐Based Nanoparticles as an Optical Imaging Platform with Therapeutic Potential
Bibliographic record
Abstract
Lanthanide-based upconverting nanoparticles (UCNPs) are largely sought-after for biomedical applications ranging from bioimaging to therapy. A straightforward strategy is proposed here using the naturally sourced polymer phytoglycogen to coencapsulate UCNPs with hydrophobic photosensitizers as an optical imaging platform and light-induced therapeutic agents. The resulting multifunctional sub-micrometer-sized luminescent beads are shown to be cytocompatible as carrier materials, which encourages the assessment of their potential in biomedical applications. The loading of UCNPs of various elemental compositions enables multicolor hyperspectral imaging of the UCNP-loaded beads, endowing these materials with the potential to serve as luminescent tags for multiplexed imaging or simultaneous detection of different moieties under near-infrared (NIR) excitation. Coencapsulation of UCNPs and Rose Bengal opens the door for potential application of these microcarriers for collagen crosslinking. Alternatively, coloading UCNPs with Chlorin e6 enables NIR-light triggered generation of reactive oxygen species. Overall, the developed encapsulation methodology offers a straightforward and noncytotoxic strategy yielding water-dispersible UCNPs while preserving their bright and color-tunable upconversion emission that would allow them to fulfill their potential as multifunctional platforms for biomedical 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) | 0.000 | 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 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".