Impact of agglomeration on the relaxometric properties of paramagnetic ultra-small gadolinium oxide nanoparticles
Bibliographic record
Abstract
Ultra-small gadolinium oxide nanoparticles (US-Gd(2)O(3)) are used to provide 'positive' contrast effects in magnetic resonance imaging (MRI), and are being considered for molecular and cellular imaging applications. However, these nanoparticles can aggregate over time in aqueous medium, as well as when internalized into cells. This study is aimed at measuring in vitro, in aqueous medium, the impact of aggregation on the relaxometric properties of paramagnetic US-Gd(2)O(3) particles. First, the nanoparticle core size as well as aggregation behaviour was assessed by HRTEM. DLS (hydrodynamic diameter) was used to measure the hydrodynamic diameter of nanoparticles and nanoaggregates. The relaxometric properties were measured by NMRD profiling, as well as with (1)H NMR relaxometers. Then, the positive contrast enhancement effect was assessed by using magnetic resonance scanners (at 1.5 and 7 T). At every magnetic field, the longitudinal relaxivity (r(1)) decreased upon agglomeration, while remaining high enough to provide positive contrast. On the other hand, the transverse relaxivity (r(2)) slightly decreased at 0.47 and 1.41 T, but it was enhanced at higher fields (7 and 11.7 T) upon agglomeration. All NMRD profiles revealed a characteristic relaxivity peak in the range 60-100 MHz, suggesting the possibility to use US-Gd(2)O(3) as an efficient 'positive-T(1)' contrast agent at clinical magnetic fields (1-3 T), in spite of aggregation.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".