Core–shell upconversion nanoparticles with suitable surface modification to overcome endothelial barrier
Bibliographic record
Abstract
Upconversion nanoparticles (UCNPs), capable of converting near-infrared (NIR) light into high-energy emission, hold significant promise for bioimaging applications. However, the presence of tissue barriers poses a challenge to the effective delivery of nanoparticles (NPs) to target organs. In this study, we demonstrate the core–shell UCNPs modified with cationic biopolymer, i.e., N, N-trimethyl chitosan (TMC), can overcome endothelial barriers. The core–shell UCNP is composed of NaGdF4: Yb3+,Tm3+ (16.7 ± 2.7 nm) as core materials and silica (SiO2) shell. The average particle size of UCNPs@SiO2 is estimated at 26.1 ± 3.7 nm. X-ray diffraction (XRD), transmission electron microscopy (TEM) and element mapping shows the formation of hexagonal crystal structure of β-NaGdF4 and elements doping. The surface of UCNPs@SiO2 has been modified with poly(ethylene glycol) (PEG) to enhance water dispersibility and colloidal stability, and further modified with TMC with the zeta potential increasing from -2.1 ± 0.96 mV to 26.9 ± 12.6 mV. No significant toxic effect is imposed to HUVECs when the cells are treated with core–shell UCNPs with surface modification up to 250 µg/mL. The transport ability of the core–shell UCNPs has been evaluated by using the in vitro endothelial barrier model. Transepithelial electrical resistance (TEER) and immunofluorescence staining of tight junction proteins have been employed to verify the integrity of the in vitro endothelial barrier model. The results indicate that the transport percentage of the UCNPs@SiO2 with PEG and TMC through the model is up to 4.56%, which is twice higher than that of the UCNPs@SiO2 with PEG but without TMC and six times that of the UCNPs@SiO2.
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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".