Bibliographic record
Abstract
Research on light controlled drug delivery release systems has shown great potential in biomedical applications. However, in general the photo response of molecules is based on UV excitation. In biomedical applications longer wavelength NIR light is preferable since it shows deeper tissue penetration and is less detrimental to tissue. We report on the synthesis of LiYF4:Tm3+/Yb3+ functionalized(UCNP) with a lipid bilayer and azobenzene. The nanoconstruct synthesised is hydrophilic shows excellent colloidal stability and biocompatibility. The nanocomposite can be used as a drug delivery system making use of NIR light excitation (980 nm) to induce the multiphoton process known as upconversion. The LiYF4 Tm3+/Yb3+ nanoparticles emit in the UV, visible and/or near-infrared. Fluorescence energy transfer (FRET) from the upconverting nanoparticles (UV emission) to the azobenzene triggers the photoswitching of the azobenzene from the trans to the cis. The photoswitching of the azobenzene molecules disrupts conformation of the supported lipid bilayer thus releasing the drug. Recently, photosensitive drug delivery systems, which make use of a nitrobenzyl-type ‘photocaged’ anticancer drug conjugated to another small or macromolecule. In each of these cases drug release from the conjugate was mediated by UV light. A major limitation of this approach for in vivo drug release is the low tissue penetration ability of UV light and inevitable DNA damage. A potential solution is afforded by UCNPs loaded with photocaged doxorubicin (DOX) onto the surface of the UCNPs via a photocleavable linkage.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.017 |
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".