Core–shell upconversion nanoparticles with suitable surface modification to overcome endothelial barrier
Why this work is in the frame
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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 NaGdF 4 : Yb 3+ ,Tm 3+ (16.7 ± 2.7 nm) as core materials and silica (SiO 2 ) shell. The average particle size of UCNPs@SiO 2 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-NaGdF 4 and elements doping. The surface of UCNPs@SiO 2 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@SiO 2 with PEG and TMC through the model is up to 4.56%, which is twice higher than that of the UCNPs@SiO 2 with PEG but without TMC and six times that of the UCNPs@SiO 2 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it