High Relaxivities and Strong Vascular Signal Enhancement for NaGdF<sub>4</sub> Nanoparticles Designed for Dual MR/Optical Imaging
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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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) | 0.001 | 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".