High Relaxivities and Strong Vascular Signal Enhancement for NaGdF<sub>4</sub> Nanoparticles Designed for Dual MR/Optical Imaging
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Bibliographic record
Abstract
Near-infrared (NIR)-to-NIR upconverting NaY(Gd)F4 :Tm(3+) ,Yb(3+) paramagnetic nanoparticles (NPs) are efficiently detected by NIR imaging techniques. As they contain Gd(3+) ions, they also provide efficient "positive" contrast in magnetic resonance imaging (MRI). Water-dispersible small (≈25 nm, "S-") and ultrasmall (<5 nm diam., "US-") NaY(Gd)F4 :Tm(3+) ,Yb(3+) NPs are synthesized by thermal decomposition and capped with citrate. The surface of citrate-coated US-NPs shows sodium depletion and high Gd elemental ratios, as confirmed by a comparative X-ray photoelectron spectroscopy (XPS)/neutron absorption analysis study. US-NaGd0.745 F4 :Tm0.005 ,Yb0.25 NPs have hydrodynamic diameters close to that measured by TEM, with the lowest relaxometric ratios (r2 /r1 = 1.18) reported for NaGdF4 nanoparticle suspensions (r1 = 3.37 mM(-1) s(-1) at 1.4 T and 37 °C). Strong relaxivity peaks in the range of 20 (0.47 T) - 300 MHz (7.05 T) are revealed in nuclear magnetic resonance dispersion profiles, with high r2 /r1 ratios at increasing field strengths for S-NPs. This indicates the superiority of US-NPs over S-NPs for achieving high positive contrast at clinical MRI field strengths. I.-v. injected citrate-coated US-NPs evidence long blood retention times (>90 min) in mice. Biodistribution studies (48 h, 8 d) show elimination through the reticuloendothelial and urinary systems, similarly to other citrate-capped US-NP systems. In summary, upconverting NaY(Gd)F4 :Tm(3+) ,Yb(3+) nanoparticles have promising luminescent, relaxometric and blood-retention properties for dual MRI/optical imaging.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it