Multifunctional Liposome Nanocarriers Combining Upconverting Nanoparticles and Anticancer Drugs
Bibliographic record
Abstract
Lanthanide-doped upconverting nanoparticles (UCNPs) are well-known for their inherent ability to convert low energy near-infrared (NIR) excitation wavelengths into higher energy emission wavelengths covering the ultraviolet (UV) to NIR regions. This optical feature makes UCNPs highly attractive for a broad range of applications including (bio)imaging and the biomedical use of light-triggered processes such as drug release. In the quest for novel theranostic approaches, the combination of multiple modalities on a single nanoscale platform, for example, combining optical imaging and drug delivery, is very desirable. In this context, liposomes, artificially prepared constructs composed of a lamellar phase lipid bilayer, have been introduced as suitable nanocarriers for UCNPs. Here, we developed a hybrid nanocarrier consisting of Er(3+) and Yb(3+) co-doped NaGdF4 UCNPs that were encapsulated in the aqueous core of the liposomes and the potential of the obtained nanocarriers for drug delivery was shown by co-loading the model anticancer drug doxorubicin (DOX). Under 980 nm excitation, a decrease of the green upconversion emission of the NaGdF4:Er(3+), Yb(3+) UCNPs was observed when DOX was co-loaded with the UCNPs in the liposome nanocarrier. This quenching effect is assigned to the energy transfer between the donor UCNP and the acceptor DOX and is most significant, since it allows for the spectral monitoring of the DOX loading and release from the liposome nanocarriers. Thus, the drug loading, release, and spectral monitoring properties of the obtained liposome nanocarriers were thoroughly characterized allowing us to assess their future potential as theranostic nanocarriers.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".